{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timeseries Prediction\n",
    "\n",
    "** EVERYTHING IN THIS TUTORIAL IS VERY NEW! CONSIDER IT ON AN ALPHA PHASE!**\n",
    "\n",
    "**APIs are likely to change!**\n",
    "\n",
    "**Examples shown in this tutorial are also in the repo `TimeseriesPrediction.jl/examples`**\n",
    "\n",
    "Topics:\n",
    "\n",
    "* Nature of prediction models of **DynamicalSystems.jl**\n",
    "* Local model prediction\n",
    "* Multi-variate local model Prediction\n",
    "* Spatio-temporal Timeseries Prediction\n",
    "* Docstrings\n",
    "\n",
    "## Nature of prediction models\n",
    "Suppose you have a scalar or multi-variate timeseries and you want to predict its future behaviour.\n",
    "\n",
    "You can either take your *neural-network/machine-learning hammer* and lots of computing power **or** you can use methods from nonlinear dynamics and chaos.\n",
    "\n",
    "**DynamicalSystems.jl** follows the second approach. This road is not only surprisingly powerful, but also much, **much** simpler.\n",
    "\n",
    "---\n",
    "\n",
    "# Local Model Prediction\n",
    "\n",
    "Local model prediction does something very simple: it makes a prediction of a state, by finding the future of similar (*neighboring*) states! Then it uses the predicted state as a new state from which other predictions can be made!\n",
    "\n",
    "Yeap, that simple.\n",
    "\n",
    "Let's see how well this method fares in a simple system, the Roessler system (3D & chaotic):\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\dot{x} &= -y-z \\\\\n",
    "\\dot{y} &= x+ay \\\\\n",
    "\\dot{z} &= b + z(x-c)\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-dimensional continuous dynamical system\n",
       " state:     [0.065081, 0.917503, 0.300242]\n",
       " e.o.m.:    DynamicalSystemsBase.Systems.roessler_eom\n",
       " in-place?  false\n",
       " jacobian:  DynamicalSystemsBase.Systems.roessler_jacob\n"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using DynamicalSystems \n",
    "\n",
    "# This initial condition gives a good prediction:\n",
    "u0_good = [0.065081, 0.917503, 0.300242]\n",
    "\n",
    "ross = Systems.roessler(u0_good)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a \"measurement\" from the roessler system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = 0.1 # sampling rate\n",
    "tf = 1000.0 # final time\n",
    "tr = trajectory(ross, tf; dt = dt)\n",
    "\n",
    "# This is the measurement\n",
    "s = tr[50:end, 2] # we skip the first points, they are transient\n",
    "# This is the accompanying time vector:\n",
    "timevec = collect(0:dt:tf)[50:end];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does this timeseries look?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 800x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using PyPlot; figure(figsize = (8,4))\n",
    "plot(timevec, s, lw = 1.0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note: these are chaotic oscillations, the system is *not* periodic for the chosen (default) parameter values!\n",
    "\n",
    "Alright, so we have a recorded some timeseries of length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "9952"
      ],
      "text/plain": [
       "9952"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we want to predict!\n",
    "\n",
    "Let's see the prediction function in action! The function to use is\n",
    "```julia\n",
    "localmodel_tsp(s, D::Int, τ, p::Int; kwargs...)\n",
    "```\n",
    "Here `s` is the timeseries to be predicted. `D, τ` are the values of the `Reconstruction` that has to be made from `s`. The last argument `p` is simply the amount of points to predict!\n",
    "\n",
    "The `Reconstruction` idea and functions were introduced in the tutorial \"Delay Coordinates Embedding\". \n",
    "\n",
    "This local model prediction method assumes that the system is on some kind of chaotic attractor. This is why it is crucial to reconstruct a signal before using the method!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use only a first part of the timeseries as a \"training set\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = length(s)\n",
    "N_train = 1000\n",
    "s_train = s[1:N_train];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then use the rest of the timeseries to compare with the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_test = s[N_train+1:end];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the parameters to make a prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501-element Array{Float64,1}:\n",
       " -1.01244 \n",
       " -0.618513\n",
       " -0.25985 \n",
       "  0.107845\n",
       "  0.481012\n",
       "  0.855973\n",
       "  1.22897 \n",
       "  1.59618 \n",
       "  1.95381 \n",
       "  2.29806 \n",
       "  2.62522 \n",
       "  2.93168 \n",
       "  3.21399 \n",
       "  ⋮       \n",
       "  6.92972 \n",
       "  7.25001 \n",
       "  7.49616 \n",
       "  7.66678 \n",
       "  7.76051 \n",
       "  7.77608 \n",
       "  7.71248 \n",
       "  7.56916 \n",
       "  7.34617 \n",
       "  7.04425 \n",
       "  6.66491 \n",
       "  6.21044 "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "τ = 17\n",
    "D = 3\n",
    "p = 500\n",
    "\n",
    "s_pred = localmodel_tsp(s_train, D, τ, p)\n",
    "# prediction always includes last point of `s_train`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction of 500 points from 1000 points. i.c.: [0.065081, 0.917503, 0.300242]"
     ]
    },
    {
     "data": {
      "image/png": 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+LXa5LY60tKSXweQHNr+fluEHjetYmJ+Hs43E/WIyj6KoIFOQWr2UukPintgcs71mR+J+GJh9RRsn54Du9vkjJ9qXyOKaPu4c3VwwZYKdeGnwJkBVIa1lcLIZiyOIEnIkArVQAAD4B3rR7dtcAPJDQ1rDQFWFMEu7TFYkU5QwE80BAAIuHkNh156oalRuXgUA9oHN76f6nJIt5+pzrU5BlFEUtW6z/k2aV7UzJO6JzTGL+8D223MVLwCJWRL3RFOIrpWsYwyD7v2bv4SGgx6wPh+UVAqxRIZKDBJNRZgzqonYB7e+nxYYDmvDh5CLJTA2195VSPYqM9Gs3oz9SK9vz9xLBJNA53cg7itsOW2UVJsqGFVv/M7tpXAsK+BfLq0ilRdxqMeHOw90NnJ5u6b9DGFE86g2cm/25ccp2kQ0HlVVEUtkAQA2nxdBv2fTuS6eg8uvWcVSMgM5Hm/KGgkCAERTqcDtvMx/f2oGz4/djtd7jkKOxaCUIv6EdejyOnDLSAidXjtGOje+78hb1E63KmK14r5ULQcApOXlhqypEaTyhhVnJ5F7lgFmojnEc2LFi4FVocg9sTkV4n4HkXtzdD9F0Sai8UiRCJIqBzBAMOABt0WtYoZhEAx5kZ0HsrwL+SvT8FH9cKJJCHNzeLvzAATOhv3+ftypqJvW1g667Vj2+VCw2SGwNoiLSxXVnojW0+13otvvBNBV8XVJVvDTS2tYTOThc9rwyC2DG3+ARTFH33ci7m3d3bqFTFxZaeTS6kpl5H57ce938vi1k8PwO3k4eevHxa2/QqJ1lBNqGRbwb/9HXjEn1T7bc0T7EpuchsxqFUc6wluXbAOAjg6tdrjKMIjOLTV0bQRhRlxcxLS/F5dDw3hT8mIrF0fQxYMtNVlL290Ql+h+2i5wLIPpSBaxrIClZGHLzqdWpCJy3799Qi3D87B1ahaVdorcp01JtL4d2HJYlkGP3wmXnWsLCxaJe2JzEiVx7+sHuB0knPj7jDGJe6IJRGaM6yzUFdx2fqgrpI8Ti6sNWRNBbERxaRlZXutIG+4ObSkQQh47OJ8PAJDm3RAX6X7aLjAMg/6g9nsWJKXtGlqJS1rQgwuFwLpc28zWsJWsOdLaGlTR+pYVAEibI/d7MKGWxD2xMVIRyJUacfi3f3sHANg9QKn7IlLtkzVPtC8xk0Dv6OvaYqZG2DQnvhJtyJoIYiMSa3GoDANwHILhwJZzgy4erM+I3EtLtMtkJVZThQpxeDV9QaMS0lKyffIlVEWBtKZVH7P17LzuO99bmquq+vlWJ1s0Ivce+/p+E+0Oee6JjUmbHiY7FfeAZs0pJLXIvapiy71ngtgl8ZWYPg4P9W4xUyM4YMxJRBINWRNBXI2qqohHU0AXwHm9CLntW84PuHkjcm93tVUVkmuBZ84tI5IR4Hfx+I07R9flTpjr3LdT5F6OxQBJE722np13ArH1Grv24vLKjuw8rSZTlAEALjvXls3GtoPEPbExZlvNTvz2+tx+YPU8IAtaMyuPtctFEe3N6NI4+IVJZOwu9G1RBrNMqL8bYFlAUZCM791W6YS1UNJppGRNALI+HwLb2ACCLjtYr8mWQ5F7y1CUZERLXWmdPLthUnSn13h5W0u3j7g3J8Ty3TVE7gFIK+3hu79zf0eF736vQeKe2JgKcd+3+byrMUf5Uwsk7omG4pqbwmh6GVwggEDH9p57v9sOn9sB5+oiPJks1bq3KKqqIpLRBFSn1972vyNxeRmZkt+e9Xq3Ffd2Gwuvz4WYw4G05Ia4NNWMZRI7IJoR9Pr2PZs0InPYOARcPJJ5EdGs0Db3GWnFsDlWY8ux9Rg7ouJye1TMOdq3fQGGdobEPbExFeK+SluO/hlLQN+N9VsTQZhQRRFS6UHCD+6s3BzPsfgUv4Lc7OsAACWVAhfY2v9MNBdBUvCjdxb17p/DYTd+6cZ+2G3tu3UuLS8jx2tCkPV64Xdt/+j1O3mwHg8KxSKK85G2EYh7HbPNptPn2HRep8+BZF6EIClI5ESEPFtbsayAtGoIc1v39jlMZXiThUdqo3KYe5n2vVsSjcUs7n1ViHufuWIO1bonGoe4vAwoWvvwnYp7oLK8G1UhsR7Pnl/RhT0AzMZyePZ8ewsGcckQ95zXC69jB+LexYP3uOETcshLCpRkstHLJHZAhbj3bi7YzcfaxXcvrRqRe76KyD3XaezQS1EqVGAFSNwTG5OuNXJvmptu7wcyYW1Sc4uY93Qi5vBB7d35Ncqbt5ApymQpVlIFXF5ZnwtxeSWN1XT7VB25GmllGVmbJu49Ae+OEvgeOtqD3/Qm8NGpn8EjFSGuUOlWKxBJC/q407tF5N50LJYVNp1nJcz3w6psOV1GlF+KWL9aTqYoIZopoiDKUNX26kOwU8iWQ2xMReS+Cs+913RDyLRHYg3RnizOreBfhm4BANwTGMQOeigDqHxoSSSYLMVrV4zqRw8e6YaiqviXS5pYeG8hiQePbOxxtjrSWgT7UkvI8C4M9Ie2PwGa757vMYmm1VXg8KFGLZHYAaqqIpLVovA+pw1OfvMSir0BJx440o2w245On/UtOUDtnnvW4wHjcEAtFiFHrB+5P7eQxCuT2jp/6cZ+HOj2tnhF9YfEPbExqVJ1Bk8XYKvixmQW9xS5JxpIctUQgsGu8I7Pm/d24vnhW5HjnXhwPobbG7E4omrygowra1kAgNdhw3X9fv3rY11e9Pg3j5JaHSkSwc1r4wCAg7eM7Pg8szXC7IcmWkOmKKEoalbAraL2gJYzcdPQ9kn+VqLsl2fsdnDBna+dYRjYOjshLixAikQatby6kRWMKjk7sci1I2TLaTAnT54EwzDr/jtx4kSrl7Y5qgr0HAO6jgCdh6s719MJMKXLKkMPI6JxpCJxfRzo23lVJjXUgTV3CFnehWSMat1bhURegMfBQS0W0XvqeSx+/reR+ofHccdoEL0BZ1snk+qCh2XBhXf+ImrrNhIVyULWehI5o3FV0L33upqKJc+9rbu76r83W8l3L8fjlu9SW65xDwAex95rYAVQ5L6hPPvsszh9+vSGx/7kT/6kyaupAoYBPv2d2s5lOcDTrVlySNwTDSSRMLzZwf7tG1gZcw3BlIpn6romonb6Ai78xi09OP3Zz4Mbv4ismEf2lVeRf+MN9P/FX+wJcc+Fw2C4nYkJVVXxmuzHVP8NsMsiPrRKFrJWkxUksAwDRVW3bUTWbiiFgp60bX6p3CkVSbWxeEUFHatR7k7LMIDHvjdl8N78rizCo48+isceewwnT55cd+zw4Soj4u2EtyzuV7VqJixtEBH1J53ULBxgGK051Q7xd3fojazSqWyDVkfUwup/+I8Inj9T8bXUj5+C74MfhP/nfq5Fq9odqqqiGI2BhRHd3AkMw+CSYMeivxceMU/5IRbgSK8fB7t9SOXFLf32ZQqijEimiHhWxFDYhaCFXwjMlXKq6U6rn2MW95G1thD3bju3YROyvQCJ+wbx8ssvIxKJ4HOf+1xbR5xqwtcLLL8DqLLWpda783q5BLFTUqXENrvLCYdj51vkXocNrMcDJZ1GOpNv1PKIKhGXlpB44gkAAOt2I/DLv4z4t74FALj8l/8FyqH3YSFZwD0Hu7b1O1sJJZnEmeAoLoeGEBgcwOfSBXRv0vzoavxhPxYZBnmbA+IKlW21AhzL7Lhm/YWllJ4Q/oFjPdYW91t0p1VVFZmiBK/DtqmesXV06GPZwuUwFUVFtmTL8exRvz1A4r5hPProo/jEJz5xbTYeubpiDol7os7IxSIyRQlgOfjcjqr+xmwcC4/HiXQ6jayoQCkUwDrbswrLXkFVVcT+7puApEXUwr/+r9D5e7+H4uQkcq+9htlEERMvnAU/OIR9nfm2EvdSJIK8zQGFYZHzBuDYoS0HALwuHozLBSWXQzZOde7bDXMn4lTe2j50cZNKOdFMET9+bxmRdBFhjx0PHevBQNC17nxblylyv2bdpNq8KEMplb/cq8m0ACXUNoQzZ87gqaeewp/+6Z8iEAjgU5/6FF544YVWL6t5VIh78t0T9Se7tAKZ1USS3++u+nyvV3s45WxOSlS0AN99YxbfOXUF73aMAXY7Qp/+NBiGQehTvwoA6M7HUTh/AQAwH89t9VGWoyzuAW1Hwl1FAp/HbgPr1q7vdCq3Z2ty71UqxH3B2uJeqqhxr1lqBEnB984sIJLWdkljWQE/PLuI9AbfS4Xn3sIVc8qWHGDv+u0BEvcN4dFHH9XHmUwGjz/+OO6//3587GMfQyJxDVTn8JmSG6kcJtEA4vNL+tgfrL5Gsc+niXuVYZBatu6D6FpAkhXMXpjCkmrHVKAPvvvu1bf4fQ8+CC4YRKiQhjI1CcgyVlLt0e2zjLRmiHuXxwV+Bw2syngdNrAu7VrNg4WSSjVkjcT2pAsifvzuEl6ZjGAutrMXTL9J3CctHrk3l1rlSwm1b88lkC5IFfPygowXL6+/Z9o6zF1qrXtPzZjFPUXuiZ2iqioefvhhfPWrX8Xv/u7v4ujRo/qxJ598EnfeeSciFn6rrQteUyJNlpLAiPqTXY7ALmsPS39H9bWkfX7jhSC1Zl1/6LVAJCMgNz4JAOjMJ+H74If0Y4zdDs8994CFimA6CnFpCam8iLwgb/ZxlkOL3Gtea4/fU9W5HocNrLu8y+SAZGEv814nlhVwaTmN16ZimI7uLBGf51i91KLVxb24WmnLURQVb81q5YYZBviVW4fgtmvfy/hqet1OhNmWI1tY42RNZTD3si1n735nLYJhGHz605+u+NqTTz6JP/zDP8Ts7CwuXLiAz3zmM3j66ac3/YylpSUsLRmRyUxGK9f39ttvw+s1RElfXx/6+qroHtssPCaPfda6f+RE+9KTXMYnx38KgeXQ//kPbX/CVfiCPn2cicS2mEk0mqVkHsLsLACgU8zC+8D9Fce9996L1A9/iHAhjdnZWfCDg1hNFzDSUZ1QbhW5tQgkVnvU+gK+bWZX4nHYwLo0W07e5oQUicAxNlb3NRLbEzfVuK+mDGbAxSNblJEtyhBlpaqdm2Yira7pY1t3N1IFEVwpl+lAtxf9QRduGAzi1FQUqgq8O5/EXQcMQW9OqJUs3KX2+IAfB3u8yBQl/WVlL2LNq2yP8cgjj+Dtt9/G9ddfDwB45pln8NOf/nTT+V/72tdw4sQJ/b/77rsPAHDfffdVfP1rX/taU9ZfNR5T5D5DkXui/ojL2hayXZHhqeEFd7QviJtXL+POxXfhT1r3QXQtsHBlUa+vPTQ2AM5babPy3H0XwDDoKCQhzM0BAFbT7WPNyUSNZmu+sL+qc70Ow3OftzksXYVkr5PICfo44OKh5PPInnoNxYmJLc9rl6RaKaKJe9bnA+t0Iui247fv2YdP3TaM2/ZpjdduGAzo3aOHQpW5TqzbrV+rVvbcMwwDJ8+h0+uAew977vfud2YxQqEQnnnmGRw/fhyxWAzf+9738MADD2w49wtf+AI+8pGP6P/OZDK477778MILL6yL3FsSj6mWc3Zt83kEUSPSsrGzxff2bDFzYwYHuyHFpgEAzjhdo61k4fw4AIBVFQzcdtO647ZQCI5DhxC+sgA5GoUqClhtI999KppCOY7m7wxVda7HwYFxmW05tMvUKszdaZ0TFzDxB78POab9PgK//Mvo+8r/Bca+PqLvd1b67jssWulJLkXbzRF4hmHQGzAqiXkcNvz2Pfs2rU7GdXVCmZm1tLi/ViBx30T6+vrwxS9+EX/0R3+EycnJLeeZhXuqlER10003we+vLvLTEpwBgOUBRSRbDtEQypF7sCxsXdWXWrV1tkdN5r2OrKhYvbIAAPAXs/DfcfuG81w33QT/pcvgZAnSyiqioeqTqFtFOpkGEAA4Dt5glbYcuw0Hu73IxWbRUUhaOlFxrxMvRe65Qg7RP/oC1Kzhu09+//vgQiH0fOmL684zJ9WakzmthFIoQCl9P5xJ3G/EVmWHbR2dEGdmoaRSUAQB7AYvO0RzIFtOk3nkkUcAADy/86Y7bQfDGL57SqglGsCLghev9RzF5dEbwNTwt2QLh/Wxlf2he51YVoBQKsEXUopwlayLV+O6+SawUOETchCXlxHPipCV9igLmUlplVU1UwvsAAAgAElEQVRYtxteZ3XxNJZl8OHre3Hr6kWMpZb06CrRXBRFRSqvCXP2hed1YV+2oQBA7JvfRHFqat25XocNNpZBwMWDgTV73pgDHLZtxP1WmLvUWjWp9vSVGF6fjuHi8t6uPEXivsmMjIwAAA4fPtzilTSYcuOqbARQlNauhdhTKIKASXgwHhrCVP/B2j6E5yGEOpGwe7GSpC61rWJ1cVX323cPdG1oawAA902aXWc4vYL9Cxdx76FOvRGNlVFFEUOL43j/7Bt4QF1D/wbNf7aDqygxSOK+FaSLEhRVhRyPw3bmdQAAFwziwE+eR+fv/I42SZIQ/frfrDt3OOzG7z54AL959z5cPxho5rJ3jPm6snV24M2ZOJ58ax4vT0Q2rGkPAAVRxtRapuIl27wjatVr9Y2ZGH42HsGrk9ZcX70gcd9k4nEtueqTn/xki1fSYMqRe1UGCtdAbX+iaWSWViEzJQ+zt/bOsv94+AH8aOxOvMRX79kn6sPie5f1cd+BkU3n8SMjYAMB3BCdwol3XsDNwyHLVh0xI8XicIsF9OViOBjgK/zXO8UWNnz6ZCFrDWWBWzh3Hh6xAADo+PznwQWD6Pit3wRbyoVLPf005FJ1uzIsy1i+S71ZiHPhDszFcpiJ5nD6SmzDHbKXxtfwX/5lEv/49iLWTMntFY2sLNilVlZUFEUt2LiXK+UAJO6bzo9//GM8/PDDOHHiRKuX0ljM5TCpYg5RR5LLRgKsuV59NTAMA7dTixLnZAZKrr26nu4VxuYu4K7Fd3BdZAqDN2y+m8kwDJxHjgAApNVVy0YFr6ZcgQSotCxUA8PzYAMBFFkbipRQ2xJSeQmQZRQuXoBHzIOx2xF45GEAmjXH/0sfBgCo+TxSTz3VyqXWhHxV5L4s2J08V1Htp4z5JXUhkTOda+5Sa71CBTnByHlw7eFKOQCJ+7qztraGJ554ArHY+pvwysoKvvnNb+LrX/96C1bWZCpq3Vvvj5xoX1KrRkTIV0N32jKeUtS/aLOjaMEo07UAf/4d7Est4+bIBDpvuXHLuc4jhvgvXLzY6KXVhUrRVJu4f2s2jsePfhDfOfQg5vLWtyLtRQJuHgezK+iLLiBYzMD30EOwhYwdleDDD+vjzPM/acUSd4W5CpMcCuuJvx0e+4a7DgMhw162mCjo4wrP/QYaqNXkRaOBlZunyD1RBX/8x3+Mj3/84zh27Bgee+wx5PN5yLKMp556Cl/+8pfx+OOPo2MXCSttA4l7okGk1wyblzdcu4fV6zWS4TIrdI22guIlzZbD+v2w9fdvOddxxOj2nTh/qcIOYFWktQhmvd1Y9HQgEay+qhMA8CwL1aO9xOYkVa9qQjSPgaAL7zv3Ih6cfwt9uRh8H/xgxXHn8ePgSh1as6dOQSlWXptn5xL40TuLePz0LAqi9borm6swZb2mlxb3xjaysNsOu02Tj+aeE1zQZCGLW8+Oa+5sTbYcoiq+/OUv45FHHoEsy/jCF76AY8eO4bOf/SxkWcZf//Vfo7u7e/sP2QtQl1rLIC4vY/krf4a53/ldxL7xDaiSNcux7ZR0PKmPfVXWDTfj8RsdTtNr1osy7XXkRAJSqVKO49DBbX3JzqOaLefHIyfx38fz+J9vzEG1eFKtFI3i1b7j+MnQCTwv1FbG2FzrPs87IVkwIrrXUVUV6VJEnuF5eO6+u+I4w7Lw3nuvNrdQQO706YrjS8kCxlcyWEoWLFkO01yFKeUyrtOQZ+MEd5Zl0FWq15/Ki/oLCxcMGp+ZiG94bivJmcS9c4+L+71tOmoBw8PDeOKJJ1q9jNbjNu1O5NrDH7sXKVy6hCuf+1dYkHgUOR49L7yM7pdfxtB/+k81lZC0Aul4Wh/7u2rfBfOZao6nItZ7EO115s5ewKy3G6FiGoGD21cPc4yNATYbnLKAZCwGQVKQFWR4HdZ9jAmxOEROW5+nxvwQr8MG1qXtMuVsDkiRCOxDQ3VbI7E9wuSk/iLqPnkSnNezbo733vuQfOJJAED25Vfgvece/ZjPVAI1U5DQabFGVuYXxpTdA0Dz0Yc2idwDQJffgYWEVmlsNVXEcIcbXMgQ91LcevfU3DUUubfuXZFobzxmcU+R+1agFAqY+Ldfwj8HjyDq0uwrrKrgjrffg/uxx9D5+c+3eIW1kUkbtgR/b21WBwDwho0IVSaW3GIm0QjePT+LU4NaictPjR7G1qYcgLHbYR8agi+bx2I8DqgKEjnB0uI+mzBqaXsCtYl7j8MG1l2K3NscVDGnyaiqivirr+n/9txxx4bz3Le+Tx/nzrxVccx8jVoycl+y5TAOB5KqYegIuTdvQtXtM15QVtMFTdz7/QDLAopiSVtOocJzb937Rj0gWw7RGChy33Ki/+Pb+EnBh6grAC4chveBB6CwHF7pux4X/ubvIa62ZxWjdMaoS+/vqT1y7+00Glll4nu7oYkVWZtf0cc9R/bv6Bz7/jH4hSwgy5DTaSRyG9fgtgrZhLHL5A5U1522jIvnwJnEPTVday55UcbX3o7ifx58AKd7jsJ98uSG82zhMOxjYwCAwvkLUPLGfcrcvCxdsJ64L19Tto4OJHLa+hgGG1bKKdPtM8oQl/NfGI4DF9ACSbLFI/euPR65J3FPNAa3qTIEee6bjiIIeP27T2HFownY7l/4EPbdcQucx49DZRicDo0h9g/fafEqa6N/bQb7kosYUnOwO2vf3vZ3mcR9ipIUm01sVbMC8LKE0LFDOzrHMbYfPlGzDMixuPXFfdqoee4N1ea5Z1kGHp9myylyPKQYiftmksyJEBcWIHA84PbouR8b4T5xizYQReTffVf/us/CkXtVkiAntCg719mJVKmmv9dhg22LXhIhNw+2lCcTzQr617lSFSErivuAi0d/0ImQm9/zthwS90RjsHsAriS8cpQA1mzSzzyDrvlxHEjMwzW2Dx998AZ88n1DGL77NoBhEHP68d4Pn4UqWlscXY2qqjh65R3ctfQePojdVbjx9RiWnlyaxH0zkSQZyYQmfIMeO2y+nUW1HfvH4BNK4j4eRyIvbHNGa8mmtegt43TC5ag9x6Xs1y9wdkhrJO6bSXx6Dmqh1LhqbAgMt7kodN18iz7On3lbH5sj95mite65cjwOlBLTuXAYt4914H2jIVzXv3UlMhvHImgS+OXk9rK4V7JZqIK1/j5v2xfGr9w6jF+/ax88Frbz1YO9/d0RrYNhAE8nkFogz30LSP7jD+AVC7h9+Ty6PvEldIa1yN+9J/Zjet8ohKkruKB6cPK10/DefVdrF1sFSiqlv5DUWje8TMDvxkfXzsIRj8I9tJ3jm6gn0StzUEq/x3DXzise2cf2wy0WwKgqpHhcay5kYfKZHOAHWKdzV5FCT1CL+ssshzxVy2kqkQvj+ji8f3TLua4brtfHhfPnja/zHDiWgayoyFjMliNd1cDqpqHgFrMrefiWAbh5riLCb66YIyUS4K+VCoEWgyL3RONwl2wPuageGSAajxSJIPvKKwAAvr8fHSeNRK/RDje6jh2GXRYRLGSQevbZVi2zJqS13Xf8LMOxDDr8btgVCQr5mJvK6sUJfdwx2LPj8+z79oGDCpdUhJxMIF2wVhTUjCoIuseXcbng2kXTHHM/B0r+bi7RyRl93HXs4JZz7fv26WVLzeKeYRg9qTZtMVtOhbgPV5fD5Hfy66w75oo5VkyqvVYgcU80jrLvXpGAIiUsNov0c88DigIA8H/4w2BY48+cYRh8/KN34GNzp3Bi7TIyP3keamluOyCsrUGBtg1s6669Uk4ZW6mhnJLNQikUtplN1IvI5Kw+7hwd3PF5nNcDLhyGR8xDSaaQE2SIsjWvXymRQJHTrDi7jdzfeqAbH1h9F7945RU449RwrZnEZxb0cecNx7acy3AcnEc0T744Nwc5abyIla05RVGBIFnnmpWvitzvFnPnXiv67q8VSNwTjcNcMYeSapvGxZfewGSgHwXODt8HPrDueHdXEIE7tXJu8loEhXPn182xKpdmovj24YfwxIH7MOXdecR3Mzhzu3QqMdg0IvPL+rj70L6qzuWHBuGRCpqnV5YsZ3MoU45a2hQJjNO5q6Y53X4nBhxAqJgBEhQNbRaqLCO+oj27OK8Hgd7tLSbOY8YLQOHCBX18oNuLm4eDuPfQ7nYc6425+lI+0IF0QYSi1L7TXtGl1kKNrNIFEX/78hX8w+uzeH1671vbyHNPNI6KcpgxoGNn5e6I2lEEAW9eiWCp7zhYlwv/674DcG0wz3P3Xcj8ROu4mDv9GlzXH2/uQmskHY1DZRjkbQ49cWs3LAZ6MN0xpjX4isQQHhiowyqJ7YhHSgKVYdB1sDpxbx8axg3nf4LjkSkcH/s9+Dbpotlq5HgcJ1cu4OTKBQTuHauomFILXCgIaWkJciIBVVW37ehL7B7hyhVkVO2lLNjVAZbd/mdeIe7PX4Dn9tsBALcM7/5+1QhkU/WlM4oP4y9dAcsw+LWTw+jybV2NTFVVvDoVRTInwsax+MCxnor7spUi9zlBRiInIpETLddErBFQ5J5oHB5ThIKSaptC8rXXscJrlTXCIwMIeja+iZUfOBLDIvLaG01b325JJ8zdacNbzNwZs55OnO06gIvhEaQiFBFtBqqqQl1bAy9LcHjd8Hic259kgh8ahF/MIShkgcX5Bq1y95ijlo5QaNdi3FZOVJQkKJnM1pOJupA6+55WAhNAYGBnO4WOw0a35eLExBYzrYE5cp9zap13FVWFx7H9ThPDMDi/mMLF5TQm17Rr0qpdaq+lGvcARe6JRuI2iS9qZNUULp96GwqjvbMfPDa2qaDI9w7iuesewKrE4cB0BIdEEQxfe6m+ZpFJmsR99+63tz2mNvJZSlRsCnI0insnT0EFwN99b9Wi1z44pI/FOQuLe5Ow2e0ukyApWAr2I+Lvg1sqYn88Dm6H5UNbwXKygCuRLNx2DtcPBHYU8bYi8XOGrSa0w9wQx/4xrVqcqqI4Pr79CS3G3DchZ3cBCmBjmR0ngAdcPNIFCXlBRkGUK6rlyBaykOVN4t5t3/vSlyL3RONwmcX93ve4WYErF6b18aGT1286z223ITF2DArDYsnmQeHS5Sasbvek0zl9HOjffYk1d8Crj3PUpbYpCNPTAAAGgG90uOrz+SFDZInzc3VaVf2RKsT9zssLbkROkPDP7lG80n89JoIDlhJNV3NpOY1vn57FqakofnJxFd87s2DZpOftYM+dxc9Pn8L9c2/h1pNbJ9Pq57hc4Ie0F9Di5GRFwQJVVZEXZOQE6+SJ6BVtWBZ5VgvwuOzcjl+6g27DFpfMi1cl1FrnOs2Lxs98N5Wr2gUS90TjMEfu8yTuG42Sz2NuVYs+2wIBjOzf3D9ut7EYGNSqzaTtHkTeenvTuVYil9XanHMsC2dg95FLT8DoGppNpbeYSdSLsrgHAPvoaNXn24eHoQK44u/D6YWsZZPjsrEkXhi4Ea/1HMUk493+hC1w2TmwLs2+VODslvIym0kXRDx3YaXia7OxHE5Ntd/OrSrLkC5eREchhX0dbgwN7TyY4DhwQPuMXA7i4iIAIJYV8Fc/mcB/fWESL41bx6ZavpaYQABFWUukrabBU9Bt7Pgm86KlPfdl9np3WoDEPdFIKHLfVCJvvo2ETUuf7RvshsO29Q1s5MioPp47Z/3tYwDIljqSepw8WHb3ty+3qX54NkU+5mYgzBh1w2sR97bubrA8jze7D+P1HI+zc9aJDprJJNKY8/VgPDSEBVSXV3A1do4Fbxb3Fo3cn19M6WUeeY7Ru5e+NZNALGutbqXbIc7PQy1qwQTHoa3r21+N46Axv3hZu7e6eA5yqQqNpSL3paZoYke33o6mGvEbdBniPpETwfp8QKmLr5XEfUE0dlCcFLkniF1AnvumMv3WOX08emhoi5kaw8cPASWBPDe52LB11QupUERe1KIvXnd9KqR4Owxxn8vk6/KZxNacnk3iJ4M34/XuIxD6d17jvgzDsuAHB+EW85BTKWQKki6arEQ2abwsljvM1grDMHB7Ss2RbHZLJSqaubxqfM+fvWMUt+7Torj9QSekNrPmmJNhzWJ9JzgOGJXhhCtTAAAnz+ovO9mivOF5zUYpFqHkNKujEDJymKrxpAfcZnEvgGFZ3XdvLXFv/Myd/N6Xvns/q4BoHebIfd46f+R7lfnLRkR09MYj287v7/TB1tUFaWUFK6k85GQSXCCw7XmtIrVqvCC6vRsV+Kweb6dxjeaz1MSqGSxF01j0dgEsi4d7e2v6DH5oEN6JFGKSBDmXRaYgVYgMK5BJZwD4AJaFJ7A7Ww4AeErXfJGzQ7KQl7lMLCsgktYi3X0BJwIuHrcMh3Cw27dtSUUrUhyfwKKnAyJrAwYPoENRwe0wMdg+MqKPhWntvswwDDwOTk8+tQLmHSAhaNwLPVVE7gPmyH1e6xjNBYOQo1FIFtphqhT3FLkniNqxu4GSTYRsOY1FVVUszGudKxmHA4NHxrY9x+OwIdSt3dAjzgByFy42dI27Jb1q+FS9XnddPtPt8+hbyLkciftGoyoKEoksAMAe8MHrqm0Hxj40DI+k7bTIyRRSBbFua6wX+dJOEON0wlWH6hwev1bZSWUYSyZ/ZwqS7r8+0K29zDh5ri2FPQAUx8dxLrwPLw3ciB+JYUhVdPKuEPcmG1q5BGNOkKGqrd9tMkfWxYDhlXdX4bl32Di9bGYyVxL3pQRyNZeDUrI2tZqyuOc5Bjy396UvRe6JxuIOA6kFSqhtMOLcHLrX5qC6w1CHj8Hr3FkUs3+oG2vvXoDMclg5dwm+2082eKW1488m8YGZ11Gw2TF036G6fKaD58A5HZCzOeQL7eUJbkfExUVkShU5/EFfzbXf+aFBeMSXAABKyqLiPlsAAgDrdNalrrbXFP03l4S1CsMdbvzGXfsqIqTtTHFiAnlbJ8AwcHaEt81hMsMFAlr0OpGoEPceuw1AEYqqIi/KLS/JaBb3Ra9R0anahNOgy45sMY9MUYIgKZUVcxIJsD277ya+W27bp3XftaKFrxGQuCcai6sk7nMxQFW1+r9E3cm/+y5uiE4B0Sl0fPT2HZ/Xt38YZ0vjxfEZHGjM8uoCm4ihp2Tv6unaXWnBMgzDoMcmQ87FEJCL1PmzwSQnpyGx2mMn2FG7Bcw+NASvaETu0wXrJCgCWuWqYimxlHW54LDtPlLoCbVH8vdmlgdVVbGcKiDktlveFqFKEoSpKeRHB8AFg/DVkONjHxlBPpGAtLICJZ8H63JViOZs0Vri/oYuJ267ax9yooRglTtqY10edHjtCLp5qFDBBSsr5vAWEPeHe63bF6IRkLgnGou79EeuiICQARzX1h9YsyheNCw1rut2Vo8ZAAaOGA1XVmeXGrG0uiFFjd0fW8fuu9OW+TC7htys1qVXyebAmRpbWY12f/mITRt16UM9tTch44eG4BY1G5WcSlpO3MvxuN7ZlHE66yJm3V6XZiGTZWTbLPl7ci2Dn15cRbog4eev78WR3t0lGDcaYXYOgqRA5Gywh8NVlYYsYx8dQf7sWf3znIcPVXyOFXz35sRsR0cIATePAKrPXXnfaOX92KrlMK8lSNwTjeXqcpgk7htC4bzRSdF59OiOz+vvDuBDbASeS+/BDRmqIICx16cSTb2RTZ0UuXBH3T63cgs5bklxv5DI48XLa4hmijg+EMBdBzrb0jcamzNeIMMDtTch4/v74ZY0ca9kMsgUrWXLkeJxFEvinq2XuHfYYHfwsCfSUHPt1U2ZZ1n9BWw2mrO8uC9OjKNg0+6DtlAY3hrEPT9sNGgTZqbhPHyowp6VtUA5THOTKdsuuyibIXHfekjcE43l6nKYoZHN5xI1oaoqIpen4IR2g7ZVsQXq5DnsH+lB6tybALTkr2rLvjWLuUgWMU8nnLKAkXo+iIJXdVQcrL48YyPJFiV8/8yCXj/8zGwCdhuLO/fXHvluFbFVY/clPNRf8+dwXi9cLgdYVYGcySBjkdKCZeR4AiKnPV6ZOtlyjvX58dnsJRSnLoHheUvt4kytZfD6dAwBF4/jAwEMhioT3vuDTvAcA1FWMRvLWWrtG1GcmEDOpvUV4DrCVSWYlrGPjOrjsu/eY7LhWKHWvVzRRbme91TDNmmFsq2CpCBdEOHkOTh5bsdVj9oZEvdEY3FRl9pGk1tawZPh4+CDR3BkIIhDVT407fuNyjrFqSuWFfdvZVlMD90CALg52JgHkZxo/YPoak5fienCHgDuPdSJm4fq9/03k2SslAjKsgj11x65BwC+twdd+QQgZtDnt1ZFFjkeR18mArdYgDvA12WXhWEYXYCpomgpC1ksK2AxUcBiooB9nevLfto4FgMhF6YjOaQLEmJZAR1ea/3OzAgTE8jbtPXZwmF4HdXvvGxUMWco7MLHTwzC47DVtBtQb+S48Ux+t2iHYy4Bn9OGsa7aSrcWRBlFSakoqaykWl/ZaTlZwBNvzQMAbtsXxl0H2i8wUi2tv7qIvU1F5N56wmkvsPDOBagMA4HjwQ1t37zqahz7TQ1XpibrubS6kstqJdVYqHB31E/cTro68fLISRRtdnx0KY7r6vbJu0eSFZxbTEKVJDDRCD59Qye6hkOWjnpuRTKZATgvWK8XQe/uurbyvX34wM9+BgA42GctoSgnEjgemwYA9A9/pm6fWy4xWP7/sIq4T+YNW5S57rmZ4bAb0xGtYdJCIm9pcV8cL4l7lgUXDNTsuS8jlmrdu+02uMPWkV3lqLoK4NWIDCWxik6fo2pxr6oqvvbiFPKCjE6fAx8LGLYrOdl6cV+Qrq0GVgDVuScajTlyX7BOQ4u9xOKlK/q4f2x4i5kbIwyNYjwwgDe6D+PypHWTarMFTdy77DawXP2qbYgeP2KuALK8CxmL1Q9fThUgCBKS3/sefP/trxD95Mew8Pu/D0Vov7KdciqFfcuTuC56BQddatXl9q6G7zMaYElL1rpu5aThiTfvDO2Wil0mC9gdypjFvd+1sXjtDxqN5xYT1k0IVmUZwswMcjaHFoFmuQo7zU7hfD5wYe35Zy6HaSXKnnvJ7oRi075HZw0WMoZhYC/tTqULYkXk3vy30CrM5VmrKWnazpC4JxqLyxRhpS61DWF5elEf9x/bv8XMjcl39eG1/uO4GB7B9Errb8QboSgK8kXNo+px1jfh1x0ykryzFqsfPhfLI/faa5BWV9FbagSXfvY5rPz5owDQVjWbxcVFjKRXcPPaOB7q3L3n2tZjiHtxeXm3y6srFeK+jl2fX3MN4IWBG/FK33UV3UVbTaok7u02Fq5Nkoe7fU7YSl7nhYR1G8aJS8tQBQGcqsAX8IJlmJotNGVrjrS2BiWXq+cy60L5BVEOdwLQfje19mTwObWfUVFUILqNyL9sAVuOuTJRPXpOtAMk7onGQuK+4awul3yTNhv6jlQv7juDXrB+bRs1GktDraITY7PIJdNQZG1dbk99t/O9pvrhubS1HsCzKwnk33kHANCbNfyx4z/8Z/zo6dfxtRcnkbZgA6eNEBeNl1C+v/ZkWv0zTJF70WKReymZQPm1i62juJ/l/Zjz9WDR02mZ/BBVVZEqVcLxO22bvrRxLIPegGbFSuVFy163wvQ0AOCmyAQ+N6Di9x48oHferZYK3/3sLABgLpbDewtJvDXb2t+fqqq6uJdM1cc2eznbDp+pcWLOYdjF5GTrX0ILppwlq/dYqBck7onGQuK+oUipFCJZzaIRCnnhcFT/EHLZOXiDmrhPMHbLWRwAILMS0ccej2uLmdXj6TCsDlYS96qqYv78JCDL8Ih5DDzyEXR/6UsAgEVPB958+mcoigour1hrt2EzxPkFfcwPDOz682y9vVhxh/DUyEl8Y0rA23OtFxFlYqk8vn34IXz3wP14tY63PVfp2i9ydkgxa3y/maKk7yD5N/HblzFbc1ZSxYauq1bK4h4AHPv2gWWZmneZzL57oeS7/9lEBM+eX8GLl9egtHDnTc3loJbsfWJw9+Le7zR2N7IqC8al/a4Vi9lyarEdtSPXxndJtA4S9w1l9b1LkFntZtzTU3vt945OTdwXbA6kxqfqsrZ6kl4zotYev3uLmdXj7TSu0VzOOnaBgqjAdmUCjKoiVEjDe//9CH/612Dr68O+5BKE6WnIqRQmVq3brdRMan4RGd4JBQzsdRD3fF8fVDCIugJIp3O6NcQKFNI5KAyLgs0O1bm7xGEzbp927asMg4JFbDkpUwOx7cR9t0/bdbPb2ArBZSXM4t4+Orqrz+IHjLK65Z2rcq6JqgL5Fv4MJFONe9FUfcxRh8h9uiCCK+0GWyKh1izuKXJPEHXAZUomI3FfdxYvT+vjnqHaW3x3m8oSrkxMbz6xRWSixrXj8de3EZrb5wVKyWT5nHWiiS47h1889SR+9dJzuCM6Ds/tJ8HY7Qj96q/CL+YQKGZQeO89LCULluh2uR3nVzL4/v578e3DD2HWtfsmZHxPD1ymRlbZYuvrhpfJZ7QdIMZuh9NRvxwRj9/wMmcTrRdNAJAz/dx9JW+6qm4ckR7ucOPX7xzFv7l/P44P1M+uVE/qK+4N+5kh7k0R7hbWujcnZIs+4zlduy3H+L7SBUnPNbFaQi2Je4KoBxwP2EtijMR93VmZMXzMvWO1N1/qGjEeQqumBF2rkI4ZQsYbqq+45zkGfEmA5YrWqUIjp9MQFxbAQUXw2GGwbi1qG/zExwGbDQOZNRTHx6EqKq5Esi1e7fYkSr9DlWXh79tdjXsAYD0eeFxaJFhrZGUhcZ/TqsEwDkddS++5Aqbk74Q17FhZ84vlO2cw+eEPY/L9DyH19DPr5jpsHEIeu6VLuQrT00jzLjx/8C48uyTi4nLtL1Hm3BJxQbOlmatEtfKl3JyzIXmNF63dJtQClZF7tViEUmjtjmhB1Dz3dht7TTSwAkjcE82gbM0hcV93VpcML3r/0QM1f4cj4k0AACAASURBVE7PQcMbGjF9plWQUknYZc124euoX2lBQCvj5irlKuSL0qZRx2YjTBo9BxwHjN+tLRyG5847MJhZg5LJQFxexnTU+uI+ndDsQ6zXC/8ua9yX8fR0gZclKJkMMhZJ0FRVVd8BYp3Oupbe8wQNcZ/PWON33hdw4q4DnTgmJ1D80/8dwsQkxMVFLPzBHyD11FOtXl5VKIIAcWEBOd6Jtb59uLicxlq69t08W1eXvitYjtybI8ettOWYI/eCx7iu6pFQmypIYIPmcpit3WUq/5yvlag9QE2siGbgCgLJWU3cqypg4ahNu3HnuRdwNJlFqmsAHQO1R0M7e8Jg3G6ouRwiEWt4ec0cyyyjb/ynkBkWI/2/XvfPdzt5pAAUGQ5yOg2b37/tOY2mOGnkPjgOVFZB8v/8L6DzxZ+BlyUUJyYwPzoIVd19eclGIWcySIsqwAE2nw/uOj1kbX29cM0WIXI2pBNpS/wMlGwWQiluxjjrG7l3+7za/VNVkctYIz+kx+9Ej9+Jhf/8XaRylRaM5T/7c7hvvx22UHt0VBZnZ7Wfrc2h9xRw11DjvgzDceD7+iDOza3z3AOtjdxLMVMeU8CHTq8dBVGpWdzbbSycPIeCKFfYcgCtYg7fs/vdulr59TtHkRdkSG1UOni3kLgnGk85cq9IgJABHPW1VVyryMkklLU1BAD09QV2JWp8DhucAT/yuRziolabmLOAwC0jx6IAAE5VYO+qf+vw404BAysX4ZBFSMmUJcT9D8+vodB/PUKFNIbGKsW97/0PgrVx6M7HsTQzg2xRRjQroNOiXT/FhUVkeS1a7/e7wdZpa5zv6YVragophwfFZBqCrLS8SY2cSELgtCgm43DWNVrosnNgHA6ohQJyeevkh0jxONJPPw0AYP1+uG68EdmXXoIciyH2jW+g+w/+QJ+bKoh4dz6J1XQBox0e3DxsHeFf9tvnbQ5wAU3c11rjvgzf3w9xbg5KOg05na4Qzy215ZgSak+OBuG9Y3TXn/lLN/bByXPwOmxIvmyI+1ZXzHHy3DUVtQfIlkM0A6qY0xCKExP62H6wdksOoFlTRkMODKZXMZReRbFUk9kqSFEjymQLh7eYWRtHfByOxmcxlloC0q1PVFRVFdORDKb9fbgS6F8Xuef8frhvvhm92RiUZBJyIoG5mHXKeF5Nbm5eF7z+YP1e7vm+Xrgrkmpbn1gsJxMolr5X1uGAo46l91w8B9ahvcBZKfk7d+oUVFGzRQUffhh9f/YV3Y6S+PbjFQ2cJFnF6SsxTEdymI9bq1NthbgvRe49jl12Uu6vTKo1e9qtYsup187KYMiNTq8DTp6rjNxboJHVtQaJe6LxkLhvCMWJjT3ZtfJzwx7cv/A2TqxdhmSqSW4FypF7xukE465vKUwAFmyXrkAoRdY8rApbz/pKSN777kVPqWutMDODpaQ1bBobEZ83OsgGOuuXM2Hr7YNb0kSunMkgU2h9Uq2STEJkNWHLOOsbuQ+4eBxgcjgSm0FnbNESDecSOQHJcxf0pl3uk7eB7+1F4Bd/AYD295T68Y/1+SE3D3vphWd1F372RlAsifuczQmu5BmvR+S+jLiwUCHucy2N3BvPYq4BtikuYOx+yonW31OvNUjcE43HyuJ+4U3g6X8HnP9Bq1dSNecuzeNs535c8fcC+3Yv7u3DQ/pYmLNW5P55pgeneo/h8tB1DfFUV4r71keZ0nkBSkZLQPWFfBt+z5577kWwmMGtyxfw81Ov4EPX9a6bYxUSiyv6ONhTP1sV39cLV0ncKxapmCMnk6bIfX3Ffchjx/22JN63egn9mQiUdGsr5qiqim+8MoP/NiPj6ZGTAADnddcBAEKf+Yw+L/mPxv2VYRh0lexjqbxoqTKuG0fu6ynuF+HiOTh4FiE3v+tdgd3QeHFvjch9LCvg9JUY3plPIJKx1stkIyHPPdF4rCru3/0u8MRvaeNT/xm4598C7/9ya9dUBZcX45js1Owat4+M7vrz+CFD3Iuzc7v+vHohCCKmWQ8Q9CAf9mx/Qg2ofj+yNgcEjkcglkSrHfep1ShQisp6A94N5zgOHQQfCuJwfA7sG0mwUAFYM6E2sRLVx8H+2vsxXI2tuwfduThOrFxCVyaEwXB9uxfXgpxM4pbVy8jxTgR676976T1zLoycTFaIqGZTEBUoqgJpdQ0OWQDX2Qlbt5Y46Tx+HPZ9+yBcuYLc669DmF+AfVBrXtblc2AhoVlyotkiBu31342rBXFGC2oUAmHY7XY4eBY8t7sYqLkbs7i4CJ5j8W/u330wZrdI5VKYbje+8+4q7DYWXV4n7j5Y+8t3tihhJppDVpDg4437lpxsXZGG1XQBL09oFeDuO9xl2bykekORe6LxVDSyskglFkUBnv9K5dde+n+AmVdas54aiEa1qB3vsCPUv/tKBPaSuFcBZOfnd/159SJj7k7rbsyNeYL14XsH7sM/7bsTE9HWe9eTy6v62B/c+FWDYRi433cCAKCkUiiOjzdlbbWQipb+7hkGwb6uun2urbsL4WIaR+MzGF6bgd+5dYfUZiAnU+gqJDGSXsHxgfq/JnIWKjGYFSTIqTTUYhEuSYDzumP6LhPDMAh89CP63PSzz+rjDq/R2CuWtUZvCSWfh7S2BhWAENau0d1acoCNG1lZgXJCLRMKYzFRwHQkh6Xk7nIgEnkRz5xbxs/GI5hVjBftVlodzTtDThsHVVWR/MEPMPtbv43Vv/h/9VyRvQaJe6LxOE3ivmARcX/lBSAxs/7rT/+RVq7T4gjxBJKCFtkNB711saqIHh/+6eiDePzQ+/F8pvUR0DLpVSPq6/Y1JnLvNnW9LVigfnhqxXih8XduvmXuOnFCH+def6Oha9oNRyfexMMTL+IX8lcw2FW/hFrW49FzMKTV1W1mNwezkGlEVJ31+zUBynKQU631MucFWbd3OGQBzsNHKo77PvghfZx+/jl9HHIb4j5qEXEvzGm7lSJrgxrU/uZ2UwazDN/To5d/LjeyajWqokBOaM9iOWR0i3bs0kLmNf288najl4XSwpfQcgMrQKs2Ff8f38LiF7+E7MsvI/r1r2P+D//QMr1N6gmJe6LxWNGWc+5JY/zx/w70XK+Nl84C0y+1Zk1VsHpxAmrpgdHRXZ8ERYeNRa6jBzLLIZEVLBPRyESNa8bnb8z2vTtkRFjzmdZX8Eibvmd/9+bVgdzvuxUqgBV3CK++Na5vP1sJJZuFmkjAIxUw2OmvqwedYZj/n703DZLjOq9ETy6VmbVXdVX1iga6gcYOkgAXkOAuShQtm7I0lE1Z1owsz8gyLfnPcyiCilA8W1uEX/iHw9Z4LIthWTG2LNszkm2RFCXLpASBFEkRXLAQOxpL72t1rblUbu9HZlZmgQB6qbyZDbFOBIMXQFXm7a6szHPPPd/5wNrRqNr8vG/HbQdeCwJNIFL1B2YPvrP9YXxv5EEYIaeQyKoOo2rVhvC62mJBAQB+8zC44WEAgPTmW9DshUCLcl9bH+ReHXetiPsLHG7ekMZwvn0xgeK4plVpvSj3RrUK6Jai3ULu20x28tYQSKy7yxqmci9rrnLP08DiN7/Z8u+1519oKfj+ZUGH3HdAHuvRljN91Po/RQPbPgDc9/+4//baU+HMaRWYu+A+iAo+2RwoikImbT3M6iyPxjpRmaqL7jUTv4ZFpV3Es67CKtXDJ/eVovswTPde+/MVdmwHE4/jld7deGVGwZtjS9DXWaMWL6G5kvz5gUihGxLDYaEBjE0Xl38DYSjlCibjecwLaYiC/z096FgUJkVBpxnIIaeQSKoO3S7q5XW1pXjUQfK9D1kDw0D9pZcAWIq4kxqzXmw5DbvOiDM0HNhawHt39uC2Tf4Umjq/F71YhCFJeHuyjO8fmcS/HB5DSQz+5/c2sNIyrnjQLrlnGbr5udbBNHcswiT3iidu1HjrdWjT0+94zfwvoT2nQ+47II/1ZsvRNWDutDXOjQBcDNjxQSBhp42cfg6orQ8V8FqYH3OjBQub+nw7brbLIiM6zaB4cX347mtFV51MdJEpHox5jiutg+ZA1bJlDaJME8n+a6fgUCyL6K23Ii+XYYoilMUlLK6zRAjvItHrP/YLbHcBP950B54bPoDv/+LC8m8gjHJFwk8Hb8V/DN2J1xb9T++JxV3LnFgKNy1HVg0YNYfcNxDpf+e9KH7vvc1x/ZVXm+OuuKXe1xQNcoh57w5UT0JYZHCjr8duScyZnkax3sCF+TqmSjKqIcS3ehtYaSn33udHAzgnXajeMJo7V2Gm5Xh7CaiHDjbHA//za4jffbf191NTqL7wk6CnRhQdct8Beaw35X7xHKDbBKhnj/V/lgNu+S1rbOrAyX8PZ24rxMKsa7/o3rLJt+N29bhbtMWx9UHu6xWXwCS6/MtI9yKajAO0dTuU5PCVxE3FCWxbGseW8iSEvuuny8Ruuw15yVLG1KmpdZd3XxufxFuFrTiTGUSx4L9yzxa6IWjWZyaVa9D0cLPfpZpdkM2yiMaF6794DYgmXWuaWAmX3HuVe07XEOl7J7mP7tsHym68VX/1laa/eVtPErdtyuLhXT0gkG67ajQ8CWHeWGA/0JKYMzmFmCfrPoyFjV5ybX9Gyr2n8pH2KWHCtubohgk1Yz1PQrXl2J57igLMs2eafx+/6y7kPvU/mn9e+qd/CnxuJNEh9x2QB5cAKPtmth6U+5m33XHvHnd802+64+PfDW4+a8DioqWEUDSNwmb/HkRdA27qzsLE+ihQrFfcAtfkdfzn7SDC0GB5S0mUlfC3Z7dPncL+2VM4sDQKepmizNjttyHnkPuZmXXXGKg4NYcTuWEc7t2Js4J/STkO2O4Coja5N8Q66iHnpjs1GzTPQ/CxO62DeMqNGPR+N8KArOpN5T6WiIK+SoM5mucRvXUfAECbmoZqd7/eO5jB/dsK2DOQ9kUxbheO515JpKGn/c19j/S5u2/a7ExL3UkYXWq9Gfda0rU6+tFNOe4pqlVsP79RqYTWcM1ZPPEMBfXsWQDWYotJJhG76y5wQ0MAAPEXv0BjHaXEtYsOue+APCjKLapdDwW1s8fdsVNICwA9u4GCnfYw/ipQncV6hKFpKFYtdTaVjILj/Iv/yw+6CtOSJ6UmTNQ9Ba6JQu46r1w7KIqCYD+U5PXQCGnR8sSyudyySUjC7t3IqCIo04Q2P4/5dUbuyzPuLpOfMZgO2O5uCPZOnFEXITbC/fwk0fpuUoLQdvrI1RDzkHupGm59iCQ3YNjpUonua+ejx+860Bx7rTnrBaauo2HXhry54wD++uAo/tdPz6Mi+7PQZ3tccq/OzLYo92F0qfWSezXmJfftX68JwSX3csoWY0wztIZrzuIpItZg1K1rld++HYAljqUfe6z52spzPwx+goTQIfcdBAPHmiOtgzbUS54IzPxWd0xRwI5H3T+fXZ9fdHliEsNLk+ipFzGQ9Tc9Jrepr2lPKS6G36kVAPpL09hcnsSG6hxieTLKPQBEeWuRJOtmqMVVpq43t7GZruV/XjoaRWLzEJINEfriIhZKdRjrqKi2NO8SiSwJcl/wKPf1OupKeMq9IctQbFsQzfMQfLA5XIm4J9lJFMMl9+/JU3h09Od4eOww4tf5bOMH7mqO66+uP3KvTs8A9ne+UbBscA3NQNSnxVmk17XWabMzzaJTIBzlXvMq93F3seiHcu/tDaAkvT0Zgn/2m6aJfIJHLsEhseTW0fHbtzXHqV/9QHNc+cEPAp0fSXTIfQfBwCmqVcqAEXLxVMUTR5a6osBvx6+64zPrk9wbly9h/+wpPDz+Oh7e6G/uezougE1aRbXlcn1d5P9unz6Lu6dP4L21i6A5bvk3rBGC3QBJpxkoIaaQNEplaPavfaVt4YU9e5BVqoBpQpmfRzGEBI5roWwn/9CJBFIJ/6NM2e5uRDVbuRfrLU1rgoZeLqPBWNcRJQhE7CZCIubWh4jh7tJEFheQadTQIy6B7712Yb+waxfohEUixVdfbVo0TNNERVYxXgy3cZy3mFbJWosUIcK03Z3WAdvjknt1drZl0RDG9eotqN3Ul8Gdw13YO5hBKtr+LnBSiCAVjWAgE7VqmZxzhpB1T1EUHr99EJ84MIT3VUebfy/Yyj0AcBs2QLj5ZgCAcuZMs9/BjY4Oue8gGHiLauWQ1XuH3McLAHtFx9O+fW5qzoWDgBp+LOKVUC5ebI6dDGm/wNAUkgmrCLACFkaIhVAO9EXLHrQSFbsdPBiV8OHRQ3j87E+AEJsDjY/P45+3vw//tO29ONG1smJpYc9uZGXr4anOrR9rjiGKqNnWBjqZRFJovynQlWALBQi6o9yLqIdoy9HLZSgOuSek3Mc4tlmgKknhFk/rS26kIpu/tmWOYlnE9u+33lMqQbG9z//65iS++eJFfPeNCShaeIsyp5jWBKCkrAV1gvdvYcZks6Ai1nWhzcy2Kvch23I2b+rB3SN5vGdHN9I+kPvhfBz/495hPH7HILZkXDEmzKJaoDW1i9vUel9NPvRQc1w7dCiwOZFEh9x3EAzWSxymoQNVO+f2StUesBSxre+zxpoMjL0S3NxWiMbFS80xPzzk+/HvyRh4aOwNPHL5F6Fn3RuK0vRJMjmy5D6biSOhyuAMLdTmQPWi9eDVaQaRxMpy0qN79iCrWM2EtLm5dUPu1elp1Flrscikki1+XL9AJxKIslZdglGvh+q5N8plqLT1M9KCAIGAch+NMKAF63cq+eQJXyu0Rbcuh8le//sZ239Hcyy99RYAtCz2ymJ4P4uj3DdoFqYd3+hHd1oHFE031Xt1dhYcQ4OhrWtWDCUtx30GMxkyCWQAwKTdY3ubu4UBbcatoWN7W+OFEw/c3xx3yH0HHawG66VLbW3OiroEgNQ1Yvm2vNcdn3+B/JxWifpFt2bAb+UeAEYGutAvLiLdEK/a8CNIqIuLMGA9BNkuMsW0DrzdRMNUmepL7sIilk5c55Uu+O3bkdVEZOUqhsZOoT/jfwTjWqBOTkKMWHOJpRK+2Ry8oCgKCbu5mSGKoXrur1Tu/YgWvBIJgcV7tWk8PHYYN02cCK0+pKEZeHOqjtF0P+aFNNhlFt+xvXubY+mI1UQwG3eV3aUQyb2j3EusAMZOp4rz/i5EWdt3b5TLMCWpWVQrh6jc04lEc0eBBJiUK06EVVDrQJu1yD3Fce9Y0PA7doAtWHYs8dVfwJDXV5zwWuC/jNJBB1fDesm6v57f3sHmBwFQAExg9Kfk57RKfFfJojHyILoZDTuXiUlcC1oaroTcLn1sbA7/tP194HQVd2YGsYHgubwqU5h2pHrZfQjG0itT7mlBQHbzJvzaqVeAMRqbE/8fqemtCsrEJERbuU9nyXQXBoBEPgPUAFNRoITYhEwvl6Hbsb80T8ZzH2FoDMeAumjv8FSrYAlb1q6GuqLhlSUTUt8eDFWmcfsyc+B37QIVicBUVUhHjgAAsjGXWIbRqdWB47OW+CgYuzYg4TO5j/T0wjF5qrOz2NWfgqabvi8iVgKH3DPZLKSGDpahwNLUsslcq0WLYBJCT4bzczUcvlRENMIgXWmgF1b9w5U/J0VRiD9wP8rf/R5MRYH42mtI3H//1Q96g6BD7jsIBsI68dxXPDaTa5H7WBfQvw+YehOYO2F1q034n/KxFkjlCqqKDrAc9G4yc2oh95Phkvva4hJMioLCcmBT5MghAFRjaZzJDEJhIqAWqiDTC3d5eLPLY6sgxNE9u6GcOgUYBuTTpxG79VYS01sVyhPTMO0Haabgb3a4F0Ihjw8d/RkErYEdA58jdp7loJfKeHj8deigkP+9X2laL/wGk2pNIQmD3CuaAUOy6Cqna8vOgeY4CLt2QTp6FI3Ll6EtLSEddQusw1LuTdNsZu+rfRsA2lqQxX303AOucg9YKvLdd/m/67oSmLre7BjLZDL4h1cvoa7oyMQi+N17/JnTy+cXML4kYmkugntBgYEJoxq81bEsqZgpyzBVFTvsRX+k5+pNARMPPIDyd78HAKj97NANT+47tpwOgoHgoUpheu5byP11umUO3+eOL71Ibj6rxNyZC81xLk+Ifvb0YzaWxWi6H5emw+1LUCu6D4R4ZmUq9lpR5OM43LsTxwojmFwKr5BarLrJIYncyv2wwm63IZv89tvXeWVwkKenMVCbR0auIt9PboEc6e5GUpUQMXVoc+E1X2tGmMJEklA3ZQBN6wiA0OpDGh5yH9HVFRW8R73WnKNHkVkHyr1eKjXretQ+d2+QhHLvQJ2Z8fXYq4FeqQB2ChqTzUCxO7iyPlrmFusNTJVkVKkIZDu0IgzlXrHrGYx6DbxmLR7Za5D7+IEDgG1R+mXw3XfIfQfBYN3YcjzkPnnt6DYMeVbt64ncX3BjugqEyJKa7cJ/btqPV/r24EQ53NjSWsklLskuslp6LO2q5FItvGg+qe5p2pVbudot7N7VHFdPn0V9HTTjik6N4T0Tb+HRy6/igTu2Lv+GNcLxywKANj9/nVeShbdWgyFgmXOwFM9iIp7HhVQftJAsZIqmu+SettKQlkN0n9d3fwQRhm4W1Yal3DuqPQA0ut1nAinPPdBa3Bk0vDGYyGah2T0x/Mi4d+AsjCieh2iT+zCUe6eHgFGrgzdsct97dXLPJBKI7bM6Kavj41BDDpNoFx1y30EwaFHuQ7Tl1D1dV5O9137dxrsAO/UCF9cPuZ8fdxWfwqZr2IraRDIRBRO38onLIXfArFdqzXF8FUR3LYh6dgakEJsDSXW7w6lpIrqKpl38yAhqkSj+bct9+Lt5AS+eC4/kOnBsXWyhQLRHAdvd3RyvB+UeIEvuD9NdODh4K17uvwnSUjjKvaIZMG1yH43HVuTXjt5yS3MsHbWKajMx67qQVR1yCMkxTjEtANy5MY2P37URH943gFzC3+s14kloUWet+7hhmJAaOhqa4eu5rge95O7GGmn3nuorubcXbDTPQXKU+xBy7mV7V8Ko18HpFrm/li0HQDOuFQDqhw+TnRxhdMh9B8FgvURhih5yH70OceITlu8eABbPWb77dYD5mYXmuHvLyjLQVwuGppCMW0WQ1YYBQwxPxa57FhfJAllfsXfxIInhpSVIsuUN5Q0V7Cpi6uhoFOmBXoisAK1YxGIl3MQHQ5ahL1jXa2TgOhY4H8AWCpiLZnAsvwU/nRCxWAunqLZWruLV3l14q7AVF1VyKSRCPNocSyGQJsCyPDjKvZBaWTO9SF9f0xYhHz0GU9dbimqXQrDmNDwNrJJDg+hOChjOx30vhmY9thxtZhbHJ8r42k/O4W9+Norzc7XrvNNfeGMwtbR7T/Xz543bMaIUx0PkrGeJHkJajrNY1Gs18HYvDO/ncCW8ca1ih9x30MEKsF5sOS3kfhkleOMBd7xO8u6Li9YNkqaAAiFyDwCptPWwVlgO9YnwimrrXotKd57oueIZ15YjS+EldyiydW4uwoJiV2cNiG3fhoQqArqOual5GEZ4HYa9SUvEyX0+3yT3JyomivVwPr9aTcT5zAacKGzBpTo5NTbm6fwplcOJGJSrNcDuNBtNrSyyFXB994YoQjk/2vTdMzQVSoyp6lHuuY0biZ2HzecAxiLQ6uwMOJZ2rO9N+0gQ8Daw0pPu7pKfyn2zGJmioCSsZ38YtSGy0xhNEhExrPH1mq1Fb7kFlL3DKL7WIfcddLA81ostxyH3QgZgliFOm+52x2OvkpvTCqFrOoq2kp2J82B4cjaHTNa1qCyNhec9FEVLgeUMDVyWXIEiAPACB9ouqJLl8Mj9Q5dfx0Njb+CAsvoeA8KO7UgrVnGgMj+PSohNjtTJKfzn4O14dugAftK1HaZJbqHB5POIOl1qRRFiCNnhACDXbEsVz7d0IfUb0aSbMiNWglN9vZCW3Pu4kF5FqtNNnsLvEyewsy+F/37PMP7wPSMY6V75IsEvODGYAMBtIBe2SzFM0z6mzcy2dC8O0o7kJfcaIXLvbQCmJKxzhKHcO91/OVmEYxpjstcW9WieR/TmmwHYvvuQ+7y0gw657yAYcEmAsi+3UG05drv02AoaIg3e6Y7HXiYzn1WgODYJQ7eUslwX2eSYTLd7AyxOhlf8JdokO8qzoBhyZAmwso55+6EkKeHF8mUXp9AvLmKQXz0Z5rdvR8bpVLuwiMWQFGzAamBV5hMoCUmUkl2+Z2h7waTTEEyrgNiQRNRD6lLrFEPTApmMewcxj1LuTVcKErJnURFNrfx+JOze7R7jxAnEOBbpWAQ0odjQ5eAU1OqFHry1oOD0TIVYh2fH760Xi+BNl9AHSu69tpy4+7n52XDNGyOqJKyFn16pEF3gXw2KXcsQEd1r9XrkHmj13d/I1pwOue8gGNA0wNvqTli2HF0FFFttWgm5j3UBhZ3WePoY0Khf//WEMXvuUnNc6CHrP8/2uukjJY/PP0hougHFJtlxgZx/2QuBt5V7VQ/8QQQARt2y1AAAvQo11AG/bTvSDetBpi8uhmZPAQBpYhIya+0upVYR6bkWUDSNeNIivGZdbCp2QcJUVciK9fumeL5FmfUbUU9zM2+6UpCIKBISDRG81kB0hZ57ABB2ualO8smTJKa2YhiS1ExXamzcjENnF/DD4zN4c4xMBDDrKaplS8XmOEhbjuZR7lUvufdxMRqNMKDtxbwSs8+hqjAD7PyqG2azULlJ7ikKzDL9Ury++/prrxGbH2l0yH0HwcHx3Ydly5E8N+yVkHsAGLRX8aYOTL3l/5xWge65cTx64WXcN3kU24e7l39DG8gOuA+hpfmQsu5lCY+M/hwPTLyFfZFgCIxgLyJU0NBrwS/mvHFxTHL15D4y0I8sYy1KtMWF0ApLAaA85e74ZHpW+H1rA/GsRe4NSYIYQgyoXq2iwVjXD83zECIElXtvfUhI5P4uTsSHL7yE3zx/EOnsypV7Jp1GZHAQACCfPg1TDy9u12vJ0TYMoaSEzAAAIABJREFUNsd+Z9w7iPS492120U11clJdgoBecp+/WsxdlPlpy6EoqqneS1F3lynIrHvTNHH/tjz2D3dh04K1O8OkUsvuAEdvuaWZd38jK/edDrUdBAfBQ+5NEyC4TX9VeItpV0zu7wTe/N/WePw1YOhe/+e1QhiXLiLTqCHTqKF/G9nuhl0b3ZjNckhRe8bSEgqS9SBKZfhAzpkVWEhyFbyhQimVwCaD9QBXF0u4nOxBxNCwIbX66E+KolAY3gCqbsKo1jC/UAFwnX4OBFGaXQRgkYdMH/kOz4muDDAHwDRRC6HIVC+VodAWKaAEwVeydCWi8ahVnKnroSU76Z6FKJ1aXeynsGsX1PFxmJKExoULmM72Y2JJQllS8dCObqL1Cl6oExPNcaPbLfr2O+PegTeylVqYBytYOfNhFdTevLUfw1soKKrhe/Tnvo0ZmCYg8u7i06hWgB6ywpQDlqFx2yZrh/vM1BkYsDryLgc6GkV0925IR45AvTwGrVgMpQN0u+go9x0EB6eo1tQBJYSEhxZyv0LiNOj67zD+C3/ns0o0Ll5sjrlhsuQ+nY6DFXgkGyLYxXBiQPVF9/NissHcXB9KKHj00it4eOx1MLXgr9G5+RJeHLgFPxm8DeejayPE8e1brcQcAPPj06HYiwCgtGjZ76hYDKlEdJlXtw8hl0NEtxT7WggLUr1cgmor9xRh5V6IMKB5a8ErhVT87U0/YVZpIfP67qUTJ3BhoYbDl4o4O1tFKcCkqoangZXa7eafJ3gyn11LP4b5+eY1IgdoI3M893Q8jlgiip6UgI25mO8Lmts2deH2oS5sT7u/yzC61JqqCsMu5l3Ob++gpZPykaNE5kUaHXJPEOfPn8dv//Zvo6+vD4VCAR/96EcxOjoa9rTCgzcOMwxrzlqU+9yIG5k5cbjZtjsMKJcsck+nUitq9d4OWIbG76gX8KELL+G2s7+AqQVvc9CKrieVyQVD7r2Nh/QQOn96ixT5eOw6r7w2hO1uYo48v4iKHPxnZygKKnayE5NMIhlAzQSbz0OwE3Pq5eATZPRyGYpjyyGs3AsRGpQgWI15xHBqgbxNiZbzMV8Jbzdl+cRJpKPu9VGWgitm98Zgyl0u8Sam3Be8zdbmIdg7FEHW+DjK/UpUbD9Ae64NvRL8PbWlsdwKf+YWcn+0Q+478ODHP/4x9u3bh1gshjNnzuDSpUtIpVK49dZb8YtfhKsAh4aWOMwQimrXQu4pChi43X3/0iXfp7USiJUa3lSiuJzsgbJlG9HkEQeRPtt3bxjNorMgMTdbxHiigPloGkaGvGcbAJiMl9yH0C694hI1YY1qN791K25avIBHLv0C/7XaSpyCgjo1BTFiNa9hUimkBPIOUDafa5J7uSZCDzjj3yiX0bDjdSleIKrcJ3gWn1TO4fFzP8X+saMwGsGr98+WOBwc2Isj+ZEWArcStBTVnjjRSu7F4Mi913Pf8DR0CsKWo83PQ7AXgJphQtXJX6+mYTTJ7kpV7HbhrR0yAozDlFUdNUVDY9EjEq1Uud/nVe6P+D63INDx3BPA6OgoPvKRj2BkZARPPfUUaNr6An/961/HoUOH8MEPfhCnTp1CLhcMYVk3EEJuZLUWcg8AG+4Azv+nNZ54Hegia4m5GqbPXsSxwggAYN/GftwcwDkjnk5+6swMIn3BerdPzdTxygarS3AhnsG1m4b7hxaVqRz8NSp7iniF5NriTvktW5CTrYWJOXrel3mtFurkFETWIvd0QMo9k8sjJ5XBGDp69Ao0wwBDB+PdBiyFsCCVQZsmommeqHJPURQiqRSccmmjXAZdIF/X4EA3TEwoFBrJbihMpGXHayVgs1lEBgagTk5CPn0aXR6PfSlA5b4xdhkAQMdiELkoIFn1C3EuAHI/N4f7thagmyaiEQZsAFGgRqXSbDzGZDI4P1eDbpjgWBpDuZivopFpmpBVA6VoEiLLI6Yp0ANsZPX2ZBkvnluAOjWJWxLd2FibW7FyH+npAdvbC21mBtLx4zA1bdUNBcNGR7kngD/6oz9CrVbDZz7zmSaxBwCWZfHEE09gfn4eTz75ZIgzDAmh23I8qS/Ldaf1YsNt7njydf/mswrMX3AVpnx/MA/xpnIPQJuZCeScXtQ9FpVkIRhbzgyXwvODt+G5obtwei54u4NcczPLY+m1FfMymQyYgtXNVzkfErmfmkTdVu75VJJoLKQDNp/H7XNn8L7xN/BgY4pozvzVoJfKuHlhFO+ZeAu/uT1NfHeNabE7BLvLpGg6TMVaWkQMbdW2HMD13ZuiiOi82804KFuOqWlQJ63zRjZuRN1OWIpxDBhCRJtJxEHFLLudNjeH3rSAgUwUXXEukJx/bwwmk83ildEFPHd8Gj84NuX79To6X8Pf/GwU/3cpigtpK6AhSOXeKVI2ZBmcYX22q9mtcKw5piiGdh9tBx1y7zMuXryIp59+GgDwvve97x3//sgjjwAAvv3tb2PRUzD4rkDYXWq95xRW4Tcc8JD7iXCisebGXXLdvWngOq/08ZyZPhzqvwU/2rQfZy8Hb8upe4huIh8MuddjCczEcygKKZSrwUcMyp7kEyG58uzwK8FvsXZ59GKxpXYhKKiTU7ht9gzumDmFu4azgdjIvG3ltfngezPoLQWmq1Oy14IwLWQNzYBhk3uOMkEJwqqPIezc0Ryb584iZqv3lYDIvTozA9i1RJHBwWZXY1KWHAcRe4dFm5tb5pX+w9vAislkmk2eSCyEnS61FM9BYqzi7yCvUyde1JRk8LZdj1lFl/Po3lua4xvRmtMh9z7jueeeAwDE43EMXyXRZPv27RAEAYqi4N///d+Dnl64EEJW7r0+/+gqyH00C+S2WuOZ44AWvL91YdYlaN0jGwM5p5rLYyzVg4VoBgtzwWfd1+sW0aVME4nuYCxssayrQEq14Mm9IrrnFDJrJ4j8li2YiXXhZNcmPP/zk4En5qiTk+gXF7G9NI79N20K5JxsPt8cayEIJy2FewGQ+9NCHq/27sLPBm6BshTs91PRDJiK9f3kBX5Nizd+u0vu5dNnmr77mqJB08nnvquepBwMbkR3kkdSYJEkXB/iWHOMeh1GPdjdQX3JQ+6zHnJPYGctzrn1J04zO298KmnITeVesQrPsboi4ugtXnJ/4xXVdsi9z/iP//gPAMDg4OBV/51hGAwMWMrrazdw97M1YV0p96t8+A7cav1fbwCzb/s3pxVicdG6KbKGjuxIMJ7/TJ/rD60Ugy8uFe1IPN7UAiFLABDLuucJIz9cltymU9HM2jz3AMCPbMHbuWG82b0db52fCzRHG7DIvYNIf/91Xukf6HS62XxGWwhBuffUaARxvU5GUjif2YDxZA/kgHP9G5oBU7auVUFYWz66sGN7cyyfOd0k96aJQBKevDGYiU0b8Vv7N+JT923Gh/aS3Rn1+u7LUzM4P1fF25NlzFXI32+8yj2dyTY7uJKoD4nZcaI0z0FiLeXeCDAK07nnmQ0FvEPuV9GPQdi9G5R9P+ko9x3gwoULAIANGzZc8zVOIe3p06cDmdO6wXoh9zQLRFYZM+i15ky+4d+cVgBV01GpWBaVbCwCZg1b4GtBdoPruS+Xg1WYTNOEKFs35DjPBmLrAIB4l4fcS8F3d1U85xRWsYV8JfiREaQa1memFYtYCjCBBHDJPZPLgQ7oeqUoCpXeQfxg6AD+md+Ml0eDJfjliojvjjyIp4fvwatz5Hf3onE3TUksBUvuFaUBU7WuKSG6ts+X7etrFrArHuUeCMZ33/DEYHIbry7GkYCX3E+NzeKZo9P4z5OzuLBA/h7rbWCle4guCVtOhKHBR2hQHA8pBOVesck9rchgTbuIOLVywYTmuGaqU+PSpZZ6hRsBN1b57w2AeTsyMHmdpAvebj6ydI2LZXp6GtPT080/12pWYeGRI0eQSLhFdn19fegLOMGkLbTYckJIy3HIvZBefXdcL7mfesu/Oa0AC2MzMOwHaS63+sK1tSIRF8BGo9AkCVWP/z0IKKoOTbaUrFg0mO60ABBNJQGaBgyjRUUPCpQkgjF0GBEOQhs/Nzcy0sy615eWsFRvYCBDvpEUABiNBoqlOuRoBl0bhmAYZiDFgoCVwrLUSAINHVUxWPucVBUhR3JQ4knIAcQaxjw1GWLAyr1Y9vZjWBu5pygKwvbtEA8fhjY7iy5TwXA+jnQ0gjihJlJeqOOuch8ZDMbqCLSS+0hpCUhZdrIgdte8yr2WysCJWyKV7BTnWChqBFLEuvcYIXjuecV9dtHJ1T0/o3tvaebcy8eOIfHAA/5NkDA65N5nFO3itVjs2sqwYUdRyfLVt+G+8Y1v4Etf+tI7/v6BKy6sP/mTP8EXv/jFNc40BKwX5X61lhwA6NkD0BHAUANX7ufOu51pC73BxadSFIVEXEBJklCVNZiNBijO3xbl10JtyY1si62RPKwFLEOD4yJoyApkJVi1GwDeM3UE2tQ0kC+0tVvBZrPIxGyLSrGIYj04oqtNT+N8uh8nc8PgBkeQXpKwMbe2hlyrRTybAmYBmCbqAavZUl0CMlZ32iCSeqJech/04ttjrxDW2GwNAPgdOyAetkIKBhfHseOuu9qe20rRuGyT+0ikJRmMNFhPZClTnAdSVj1XEF1q9ZIrKGqJVJPcc4TIfYxjUKxT0KMxqBSDSEBpOaZpNhdLEdn9bqxGuQfsxJz//fcAAPHIkQ65fzeD4zhoy3TzVG0VNnuNWKbf//3fx6//+q83/1yr1fDAAw/gZz/72TuU+xsKYZJ7wwCUyjvnsVJEBKBnFzB9FJg/Ayg1gF9bVOFqMXdxojkubAz2M08lBZQWgAYTQX16FolNwWxf1+ZcS0U8EQwxdCBwDBoyIDVC6Oxq59xzibUn5TgoDPYBZSvKbXGhBGwLJkJVnZx0G1glk0gE0MDKQSKXBTVThUlR1gIxIJiGYdVoZKzutEFEf0bTLlGRasHa5ryLCaGNxXeL7/70acQDIvemaaIxYd1XuYEBvHqphPElEQmexb1b80gR7MvAdrvfQ3ZhDhiyxrIWsHKfSAGLdlE0ocWokzxE8TxkloMQUGSrqpvNJnYRyf1u0PHV3VdbOtXeYL77Drn3GblcDqIoQpKunbRRtlMV8p50By+utNtU7C/E3r17kVpDnvC6QUQAGB7QleDJfaMG2L67NZF7AOi/1SL3MK3/D93j2/Suh8j0JAriEipcHD0jwSSPOEhlkgAsi1h5ciYwci8vlcBrDTSYCOKpgMk9H0EFgKKbMFQVdCSYDq+macKwLXh0ov2FY9fmjWDemINOM5gfmwbu3tr2MVeCxuQk6qzbnZZ0+ogXXD4PTi9CYTnUA7QAGLUaGpRFkCieJ9qd1kHcS+7rwRZ/d5sKdhYvQaVZZBJrL/D3JuYop8/4MbUVQV9YgClaC5TIxkHM1xRMLlnP7Pu2Xv257BciHlsOMz8LirKKiKUG+YQgr29ciyUA2OSe0GLUiTelOauoNlmtwDRN4jVU3oUSV3fvqRSzuu8l29sLtrsb2twc5ONvwzQMUPSNUap6Y8zyBsLOnTsBALOzs9d8jZNvf7WozF96OMQ6aHLfTlKOg/597jhA3/3w5bfxyNhh/Ob5g+jZsSWw8wJA2lNgujQZXC7zQKOC3zx/EB878zz2FoLxijtw0j8MioYSIEE0RbFpRfKD3PMjW5C2i2qLM4tNJYs0vMp9PJNEhAnuMcPm8xDsTGuxGpxVRS+X0WCsRSAtCES70zoQMp7YVjHY+pANkHDb3FncNXMSueTav5/81hHAJlzyGYvcm6YJqaETjW/1JuVwg24DK4py89lJwWvL0efnmwtBOQjPvR2FScVioLkIEjwLjqXJee49yr3E8oBhwKiT/14mOBa/e88QPrZ/I/bMWtcVvUpLDmDXhdx8EwB7AX/pkp/TJIqOcu8z7r33Xvz4xz/GxYsXr/rv9XodC3ZM28MPPxzk1NYHohmgPgdIARfU+kHunThMAJh6s735rAKNC9a1xKTTYLuCaebkINPtnq88F1wjK23Rql2hYYLPraKbsA/YKhjILIwioqtWAVg+mDoHsVTFi/03g9NVbEr3od09Gn7LCJKNp1EUUlCLRZTEBnIJ8sXJytQURFu5T+eD/ezYfA6C3kAZgFIT0dAMYn5iL/SSS+6DUu5jMQFgWUDTIEvBKvfODhPQ3kKU5nlww0NonB+Fcv48fvL2JE7NS2hoBn7v/s1IEGoo1ZKUsymY7rQO6HgcdCIBo1aDNjeHaISB1NADLahlMmmMdCcx0m0RXlILqV19KWwpJLD07DSUqiV4GpUyGB9sh9cDTVPIxCyRplSchQmAWWUxrYPoTTej9vwLAAD5+HHwmzf7NU2i6Cj3PuMjH/kIAGBqagozMzPv+Pfjx48DsLz5Dz30UKBzWxdwiHWjCugBepr9IPeFnQBrq1QBKfd6rQ7N3gXitmwJLBLSQf+GbmwvjuHWuTPILr7zeiYFveg2IWJzwRURA8CuFI2bF0axc2kMdD24osx6uYrLqV6cyw5iKtr+Io7fOtJU7vWl4OIwy1PzMO3rNNMT7GfH5PIQ7CZzhigFlu+vl8tQPOQ+EOU+QoO2C9xlOdjib91D7plke7tMgmPNUVWoM7PN7HWScZjepBx2wwbUlWC60zbPaav36vx881ppaAbR3TXTNJvkns20LrpJPVfiPIuuOIdYKg7nDHpARbUAYCgKTLuTMnOdBMPrIXrTnuZYOnbcl3kFgQ659xm7du3CBz7wAQDAD3/4w3f8+wsvWCvAT3ziE9eNy/ylhZdYKwE2RvKD3DMs0Gtt0aF4AZDI594qF0abY25z8Daugc0DuGPuNHYVLyM5N7H8G3yCVnR/t0HvVni3bwNtl+5JIOGF9hV2pqsLOQ7okivYOH4WUY68mgwA5TlrYUZFo0ilySp0V4It5Jut5g1JhBRAAglgNbBSaYsY0oIAPgDlXogw2EhJ2FyeRKE4Rfx8XijVOhwa2q6FjPcU1UZnXEW9THAx2kzKAaD1D8Kwles4YUuOAycO0xRF8IYrcpG05hjVKqBbx2euEeZBCt4ISiOgolrA/pmdOayxXlHY4yH3xzvk/l2Nv/iLv0A0GsVTTz3V8veiKOKb3/wmcrkcvvrVr4Y0u5ARVmJOC7lfe3OgFt/9NPmW1G8dv4jvjTyA/xy8HcUNI8TPdyXYQsHKfQegTQen3L9aNPBy3268WdgKKuAHkbeLoRFg0xWp6qqhQrT9yFGKojDSn8GvXnoVB06/hF6avLprNhqo2BGUTDKJZEBKqAM2l/Mo9yLEgBKPWpT7gNJyhAiDh8153D19AiOzozAbwcWd/usCg+9sfxj/uuV+0PE2lfsdblGtMObaWUkq941xexFBUWjke5p/H5hy7ymq5cUqohyDrjgHjWB/BG8DKybTxjNwDfBGUAah3E+WJBwdL+H0pXnIjHUvXatyz6RS4Oz6SOXUKRgBfs/aQYfcE8C2bdvwrW99C6+//jqefPJJKIqC6elpPP744yiVSnj66afR09Oz/IF+GdFC7gP03ZMg95PkfffzYzOQWB6z8S5ENgXXaMUBxbLNB5F6FZsZKVySgAvpAZzJbkQkRza94krQyRRUikGdFSCWAuyoWHUTtoS4P0XE/Ii7IFRGR6/zSn+gzs42k3LoZBJJgpGCVwOdSmFAKeOOmVO4b+YEulPB9EjQSyXsWbyIu6ZP4J6BGLiAioi9hCVIu4Msq03rVdvK/XZXuedH3cQcorYcu6CW7e2FaLqfVRDNs4DWOMwHYjKeeGALfufuIaRj5L4v3hhMJpvFqxcW8dzxabxwahYKwRjOk1MVHKEzeLtryJpHALuh52ar+MnpOfzw9DwqnJW2tlblHgCidlGtqapQzgSX6tQOOuSeED760Y/i4MGDeOONN9Df348DBw5gaGgIJ0+exN133x329MJDS5fasGw5bcSJtiTmkCf3C1Nu6lL3tnDSldieHshMBPO1BnQlmFQOUbLUkShlgG6jSc5acIFN4F+2vxf/NnI/Ts1fO9LWb8h1N4+Zj/pE7rd4yP35874c83pQJyfRoFlQpgkmGWwMJmDtVvQkOWwvjWNw6hyxgswrYZTL6BWLGClP4vahXGC1MXTavZfpAdodFHtHJGJobRdHsoUCGNt6Fzn9NmAbfiqEyL1eqTSJLjc4CNFj3QrqevHGYWrzwaSQaVco9+NFEWdmqjg2UQZN8Hp9eXQBr6sJnOmyIgKC2A11utMaSqNp02unNkS46ebm+Eax5nTScgjinnvuwfPPPx/2NNYXwrLleP39fBvkPr8ViMQBtQ5MkbflFGemAPDgTBXpwX7i57safjqwF6OKtdN009QMUsNks/YNw4QkWzfkGB8JvIg4mnIfAmIAsW0O5LpHufepcRc/4kaniudGkTJMomkg6sQE9i2cxy0Lo0g8die6k+TTea4Em89Dm56GvrQEU9dXnW29Fugl917GZNZY07MGOAkgJixrUBAwDBNqwyLenKGBbrN2jKIoCDu2o/7yK6AWFyCoMuRIFCWJjP3Bm5QT2TiImuJat8Kw5WhzwZB7JwYTAJhsBopduMzQFFiC94QYx4LiOcgMBwMU9Ar5HSandsFUFPB2cAe9xrQcoLWoVj52HPjt9uYXBDrKfQfBYl147tsg9zQD9N1ijctjQH3h+q9vAw1RQkW1/IJZRgTNBU+UACCRdX9fxfFp4ucTFRWGbEX7xWLB/8wxDzmTA2wORILcc1u24GxmA76/+V787TSL6TLZnQin6ycNE7mhDWADzLh30ExXMgzoxWIg52yxPAToZ36J68X/HXkQ39n+MKRSMLachm40/f0RXfOnJ4OnmVVsybqn1hUdqu5/YydvUg63cRM2dsVwz0geewczyMXbr3VZCbxZ94GR+yuuUYfc8yxNVECJ8wwojodJUVCYSEDKvUPuG+B0ayHKrCHn3gG/YwdgNzO8UZT7DrnvIFh4iXWgyr3nwce3mVLUYs0h15J6/thhmHaAWK795+ea4W1kVZ4m/yCqLS5ZLRthZ3kHjJinOZAsBkfuFcm1PAlJf8g9WygAiSSqXAza0hKW6mSLatVxN1EpMhhMN+MrQedyqEUELAgpTI5du5mgn1DKFcxH0yhzMajR4BKC9GgMCsvBpKjA6kMU1YCh2uSeMkFx7RNiwZuYM+cm/5Dw3XuTcriNg+jPRLF/uAvv2dHdzEYnDa9yvzhXxI/ensH3j0ziyDi5OjRvQS2bzTZ99qRjW2McC5q3RBqJ5QPx3DsRuBFNBm3bvOjE2p/7NM9DsGtDGhcuBFrfslZ0yH0HwSI0W46X3Leh3AOBdaqdO+YuHPL5YCMFvUh3u1nlpdnF67zSH9TmXbU14RPJXQ1iWfcalaTgOn96GxEJbWaHO6AoCoU+qyDZqNWwsEj2O6dOeMj9wADRc10LbD6P72++Dz8augvPnw5GFa3WJPzHpjvx7I6HcPB8MLsFABD11KOInrQlklB0HaZikXueY31RfXlvYs7k5eaYCLn3KPdhLUC9yr2yWMSp6QouzNcxVyEnJniVezqTafYTIB3bGucYUDa5l1kuEGLseO75hvv7bLcfg1NUC9OEfOJEW8cKAh1y30GwaCmoDctz76dyT66odv6S+xAqbArHbw8A2T73QVReJJ9wVPOcI54KfstCSCWa8Z+yHFzsWU4qY6gyjf7aPOJp/3pgdG9ySfbCRbK9CorT83ip/yYcG7kdE7VgMuavRCSfh2AX0dXLwRBeuWoVQ1OCAD6AGEwHMc/iVwroZ1VU15Yj8P6ku/DDw03bw8C5I/jQ3n584sAmbOryf3HfuHipOeY2Dfl+/JWAjkab6S3snLu7RLLpmle515NpZ3OUvHLPs01yL7E88Zx7wzCbuxIRD7mn4+0JZDdaUW2H3HcQLMJW7iNxyzffDro2u+o/QeV+Yda9CXbv2k3sPMshs6G3Oa4UyasuVY+6nMgET+5ZhgZnq1myElznz63VGdw7dRwPTbyFdM6/oszs1mGwdqOc+al53457JQxRxFJNwaVUH04O7sb4UnDFyF6wBbdLbb0mwjTJZYcDgGkYEO16CZrnIbDBxCkCQNSTVCNW69d5pX/weu453h8bC8Vx4LdYxd+x86cxlGSRS/BEajYaly4BsKwxVCyGxZoCWdWJXydXwonDpGem4CQEkWxi5VXuNY9FhSd8vcY5BhTLADQNiSGv3Cua4S5cFLfGqN3akHcU1a5z+PrNeeGFF/DZz34WX/va13Ds2DE/D93BLwtCK6i1iXI7xbQOaBro32uNq9NAhUyR6WLVutFTponCbfcQOcdKkOnrbirZ5Qp5AlHzJI/Es8Elj3ghRKzUDDmgDqfAFd0U21SZvOBHtiDVsIj20lwRGoEiRcCKwaxHrBoJJpUMLFbwSrC5HATdslPpotQsHCQFo1ZDg7J+Viqg7rQOommXsEi1YGJbZUkBDOt36kezNQfNZla6DuUcmdhWvVxuFllzQ0OoNTT8/SuX8fWDo/jh28H18QBcaw4tS4jYiS6OnYQE9JKl3FOCgAbrfm6klXsrgYgCxfOQA1DuvbsfnOwKDO2Se254uHlflt5+u61jBQFf776/8Ru/gYrng8tms7jvvvvwwAMP4MEHH8TevXv9PF0HNyL4kAtq27XkOOjfB1w8ZI2njwCpPn+Oa8M0DNx+4RiW6ATUJA++q7D8mwiB5yMQohzkuoxqAOkxObGMkdIEZIZDthBsd1oHPB8BahYxNHQddABxikbdslVQ0Sgo1r9bMz8yglSjjqKQgrpURElSkU/4n0LUGJ9ALWLl8zPJFNLRYBtYOWBy+ZYutVJDh0CQcOvlMhqM9XnRgkCcLHkR89jWJDEYcj/IGXj48mGoDIuBPv+e6cLOHSj/uzWWT59qUUr9gqPaAza5l90YzGiAizKgNeuek0Wo8TRRW44sy9wuAAAgAElEQVRmK/dMNtv02wMAR5rcc/Z3g+etgtrKJNHzGaaJfIKDrBoQJNeq1m4nZYph0PvH/y/YQgHCHv+vTb/hK7n/lV/5FfzLv/xL88/FYhFPP/00nn76aQBAOp1uIfv79u0LPMO6g5AREQBWADQ5OHJvGEDDIfc+KPcA0H+rO558E9j+AX+Oa8Ocv4ihnln0VxbBZsPvZpyKC5DrMuqqAV0UwcTIFbpurM4gOXMSAJDrzS3zajKICpayZVAUGuUKhC7yiwy9Zu2KMD5EC3rBdncjw1j71HqxiKV6gwi5VycmUOcsck+n0kiFRO7ZfK7puTckEaKqg+Snp5fKaDDWz0rxPNGFxJXwFn/LARV/Cw0JPZKlAqfi/l1H/I6dzfHUyVHMTFdQkVTsH+7yjSe0kPvhYRQ9GfeJgBuueRNzIlINiKeb9iC/eZFpms2ceyaTQYxjsXcwA0XT0UO4i3OMZ5CNRcAzKtKNOoxajWj/iXyCx387MAQAuPhXFyEDAEWBjrXfGDD9oQ+1fYyg4OvV/O1vfxsf//jH8dOf/hSHDh3CkSNHoOvuSrRUKuHZZ5/Fs88+CwBIpVK49957m2T/1ltvBU13ygB+6SGkgVqA5L7hYwymA8JFtXR5FH132L+fe/6b78dfLVKpOObmSjAoGpXJaWS3bln+TWuEtuimjbB258qgcTdfx56Lr4AzVFC1KhAAuf8/XTfBiG9Dd1LAVh+PS1EU8r05oAoY1RoWFirY2uNfwa6DxsQ46o5yn0oG3p3WAZ1MQoDdoVKUIDW0Zd7RHvRSCQptkXtaECAEWFAbTcUBigJME5IYDLn3eqbpNrvTeuGNw3xlooqqbZPZPZD2zeKlXLzYHHNDm1D1NrDiAib3BY9yX68CeSsBWNEM3xeIRq0GaNbPymYz6IpzeM+O7mXe5Q8iDI1P3jOMsW/No74w2pwPkyZvuTRqlnJPx+Og3mXc0termWEYPProo3j00UcBALVaDS+//DIOHTqEQ4cO4fDhw1A87evL5TKee+45PPfccwCAZDKJe+65B4899hgef/xxJNvsfNfBOoWQBmqzwZF7PzPuHWQ2ArE8IC4Ak29Yd2U/1Za5k+64e5d/x10j7s6z2PnjlxBTFXCLHwMIkntv4yEmJHKfT0fB2teNEUBHRUPXIemAyfJoEMhJ797UD7xtxZjOXZoAdvsfU6lOTDZtOcl8FpEQGlgB1mImnrJ+h4YoQiRcN6GXy1AZj+c+yIJaPgKK42AqCmQlmGQno+bW3fi5y8Sk04j090OdmoJweRRV+55allTfyH3jkhuzyQ8Po+4h90EvRr3KPVt1n4UkbGRhNVnzwttESq9WAyf37zYQvfsmEgm8//3vx1e/+lUcOnQIpVIJBw8exJe//GW8733vQyKRgGmazf8qlQp+9KMf4dOf/jR6e3vx5S9/GY1GcFF0HQQEp6i2UQV0sqoaALeYFvCnoBawiPyAbc2RloClS/4c18H8aXfcveParwsIhf4C0g0REVOHOk2u8Mw0TShFu/ArFgMdbX8rdS1gUu6Dx6iQX4TKVRFOVgdHoBA1v2Uj7po+gfdffg37xanl37AGSBOTkFgeoGlk8uEQCAdOhKopy6jLZBOP9HIJSostJ0DlPsLgHmkSD0y8hZumTgVyzomFCsYTBczEumC06WO+EvxOy5oTq5ag2/V7ZdG/z69py2FZRAYGWjz38YALwFtsORWXfMua/4tRbwwmkwmnjolOus9evRyMsNck9z5bHW8EBHo18zyP+++/H/fffz8AQNd1vP766/jc5z6Hn//856AoqhlHJUkSvvSlL+E73/kOnnnmGWzd6udGdQehwpuYo1SAGGF11s8GVl4M3Aac+7E1nnwD6Br279iOck/RQH6bf8ddI9g+Nw5TmyGTDgRYMXt/n9iJyNZBDPEGwlrWMCnPg4hwugMAyJ5zCD7FC3oR37oVI2WrkI2+5H8SiWmaWJpdBPo2gU4mkfHRi70WJNJJQDQsu8pSCQC5gnS9VELDY8sJUrlnaAo7Igrk2jwg0kT82lfijRkJJzZYtsRtcX9314UdO1B74QUkVBHawgKYdNq3RlamYTTJPbdhA6hIBDWv5z5wcu9ek/mlGSQH0ohGGMQi/s+jRbnPZmEYJmg62HpH7z2VZGLOaxeLmCyJ4Bka3Q0dcfhrH7tREKoJiWEY3HnnnTh48CA+8YlPYP/+/fjBD36Az3zmM8hmszBNE2fPnsW9996LN98k1yyog4ARdBwmKXJ/ZVGtX9A1YM5W7rs2A5Fw1GsvIr1uGpA6M3udV7aHWk2CqShWgWIA27bXQj2ewsVUH05nN2J2IYCOimX3HLzgPzHmR1wblXLef3KvLy2hplnCDJNKhVZM6yDfFcd/Of8zfOzM87g7Sd6W89D4G/iNcwfxX2/qQoQJmDSl7XuaYcCok4+qbe2k7C9pEnZay/mEKkFbWADgX5dabXYWpmzNnRu2hBiH3HMsTTw15kp4u9RunL2Ih3f14N6teaRj/n93WpX7DJ4/NYv/+cI5PHVoFEt18u6I4xNlPI0e/NuW+7AgpIhm3c9WZFxaEHF6vAjA+i4yPu8w3QhYFxUGDMPgm9/8JiiKwuXLl/FXf/VXmJ6exlNPPYWBgQHMz8/jgx/8IBbsL3sHNzhayD35jqdQPAsIvzz3gKXcO5h83b/jFkcBO6cbPesjcovq7sZouh/HcptxfJ5cc6LqrPsdTyTJJfIsh7lIEj/vvwmv9+zA+BJ5wiRV3Mg2Puo/uWd7eppb043zo74fXx0fh6A1sG1pHENpDt3JcJV7IZ9HXFPAmAa0hUWi5zLKZTAwIegN5LtzgSfAee0ORgB2B0WyyCBj6OCSPtty7MScREOCbj/vKz6R+ytjME3TbHruwyj+pnm+6TvX5sk1lwPeqdwrmgHNMFFXdLABLEYlVcc8HUM9ErW61BIk904jMLPRAKdb18670ZazLsg9YBH8L3zhC/jbv/1bAADHcfjUpz6Fc+fO4dOf/jSmp6fxu7/7uyHPsgNf0ELuyVseWpR7vzz3ABDPAdkhazx9FNB98obOeLrfrRNyz3R14dWBm3GsMILTNXIPg8qcW0wbT4V3Q455mgPJATQHkqvugika858YUxQFastWTMVyOC4yGJv0VyhpjI2hS6li/+wpfHA4TiSNZzVg8/nmWF8kKwppLcQp+FqDejKLuWgGE4kCGuUALGSyRe4jhuY7aYoM9INOJhExdbCzlv3PL+W+JSlneAiKZkDVrd2m0Bqu2b57bW6OaIdc7Qrl3ptzH4SNLMYxoGy7oZV1T57c05oK1rTH70Jy7/sVffz4cfz1X/81xsfHsXfvXnz84x/Hzp07l38jgNtvvx2nTrUWBQmCgL/5m7/ByMgInnzySRw6dKjp2e/gBkWothyfSceGO6xiWk0GZt9ujchcK2ZPuOPe9UHuIxEWsRiPek0i2siqtuCS+2QmPIIYS7vnlgJo3CXXXHLPEyoiro3swE8UywoQPX4RGwfyy7xj5fCmkHCbNvl23LWCzbv9EYgr956OykwICW+vCb04vWk/AOCWpQpIu4sbDYtsc7rWdmOgK0FRFIQdOyAePoxYcRaGLKGGKFTdaDt9qXHBG4M5BJ6l8QcPbmnx3QcNtrsbyrlzMBsN6KUSqHQGumH6bhHSW+KFs1CqFrmnKSoQG1mcZ0HxlmghszyMKrlFqNPll9cbcH6yjue+Tfz85z/H/v378dRTT+GHP/wh/vRP/xR79uzBgQMH8NRTT6G8zJbh5cuXwfNXV60+97nP4SMf+Qj+7u/+zs8pdxAGwiT3nM8P34Hb3fGET9YcL7nv2e3PMX1AKm41O5E0Ew2PjcRP1Jbcm34yF17iSjTjbQ4UgHLv8UoLcTLkvnvzxuZ4bszfxJzG5fVF7plcDpeTPTiSH8FPp2QYBjlVVKpU8WZhK05u2IXLpWCy5r2IeprzSISVe8MwodjkPmJoYHy25QAA7/Hd6/bCzA9rjrfWhB8ZsRYSEQb5BE+kqdtK4Cj3dZbHXz9/Bl974Rx+fNL/NDJtyRMvnMtBsRN5OJYOxEYW5xjQjnLPcMSUe9M0m11+Oc29ZjpRmG3iySefBADk8/mWiMvXXnsNf/AHf4De3l58+MMfxte//nWcOXOmpcHV66+/jk996lPYvn37tQ6PP/zDP8SLL77o55Q7CANXpuWQBilbDgBsIEDuHVsOnwbSg/4c0wek0tYN0qQolMfJtBCvljzkPh9OZBsAxLrca1QSyRecyXV3AcETIveZbVvAa9bPsjjjr5rduHwZDZqFSVGIbNy4/BsIg83ncSHdj7fzm3Gy5j7wSaBelXAyN4wj/btweiaA+9kViMbdDqNiiWzxd0M3YDrKvaESsTsI2y1yn2yIEIrz2JCNwo+1mXLuHACL3IbVHO9KOOSe07Vm3Y2jPPsJveix5dieewDgAyoijvEsKM5aQEkElXtVN6HbFwuvuQttv7t+3wjw1ZZz9OhRHDt2DFu3bsXCwgKeffZZ/MM//AMOHjxo5VcrCp555hk888wzAACaplEoFFCv11Gz80j/8i//8prH37NnDyYnyZCKDgJE4Mq950bC+fwl770ZYHirAHbitfaPV5sDqraq2nezv42x2kQ66y6MliZmUdh97YX4WlEruwp2qid3nVeSRSydbHb+dDzGJLFRreK+yaNo0Cx6c48ROQc/MoJUo455lkOlWIai6b74bU3ThDg2jv+z7SHw8SjuPL+ER3b3Lv9GgmBzOQj2QsYQJUiqTiTH3DQMiKJl26IFAbzPzYdWgqgnsUaski3+tsi97bnXdTLk3lbub14YRWbuTfTf/tG2j6ktLUFftBa0/MhI28fzC04cJmvqoETrsyOxEHUaA9KJBKhIBIpjXQmoJ0M0woAWHFsOOeXe+7vjVJfc+20fuxHg6ydbKBQg21FT+Xwen/zkJ/HCCy9gdHQUf/zHf4yRkZEWRV/XdczMzKBarcI0TTzyyCP47Gc/2zzeM888gw996EP4yle+AkVRoKoq4u/C7ZVfOggeu8WN7rlnOddnX7wA1NpMPZg+5o77bmnvWD4j3e0q6Uszc0TOUbOLVynTRLyHXDb5cmBYBpxNfOUG2SZIAJASy9hUncXW8iQyWTK+bba3FxnaevhpxSKW6v78XHqp1LRNGNkc0cLAlYJOpSDA+lkNUYREqEutUa2iQVnXCcXzEALMuHfgJfdyjSy5V1QDRsMiTRxlNH3UfoIbGQFYFhQA+aQ/jbkc1R4AeLtnzpmZKg5fKuLUdKVpUwkajnJPAeBES+BUCJB7zSb3TFcXVN2EYX9Hg+rJwNAUYvaOpMTy0Akp997fHd9wd0PfjQW1vpL73/md38HnP//5FrsNAAwNDeGLX/wizp49izfeeANf+cpX8Oijj2LPnj3YsWMH3v/+9+Mb3/gGnn322Rb/1yc/+Uk8++yz+OIXv4hPf/rTeOONN3DXXXf5OeUOwkDgyr3HH+43uQeAwf3uuF31fvqIO1535N4twPSm2viJul28yusNcHn/Cj7XAt5WehWFPLnX6+41SkploigKOXuBZlQqWFz05wHbuHQJNbsXA5NOIyWEm3EPWD9r3I5SNSQJ9QaZokm9XLZ6MsBS7oPsTusg5kmVkggnO+mGCU6WQJkmeI4l4temOa6prisXLsDwoealhdzbxz41XcFL5xbwo7dnoOnhLEgjni61bM36Pvq9EDVVtdk0iu3qalnIBGXLAYB4XAAYBjJBz71XuY8oXnL/7hOFfd2n/MIXvoDHHnsMd999N/78z/8c99xzzztes2/fPuzbt7JEkb6+PiwtLYGiKHzve9/DyZMn8Wd/9md+TrmDMPDLlJYDtJL7sVeBHb+29mNNH3XHfXvXfhwCyA70NMelRTKf2wMTR1CZmYMZi4MmlBqzUkS5CKqQIWsGDF0HzZBTuQyP4kqiSNFB90APULGiIWdHL2P35u5l3rE8Gpcvo8pZRJrJZIg04VkL4sk4IAKmJEGUySzQ9HIZik3uKV6AEIYtx5PsJNbJkvvetIDHR38GrVgEM7CB2HmEPbuhnD4N6DrkU6cRu7W9FLKWYtptlnJfta8JhqYQ44L/3ABXuQeASLUEFYBmmL6kAzloicHs6roiBjNAcs8xoHkeuq5DrpDplZIUIrhzuAuSqiP1uvvcfzd67n39ZFmWxfe//3186lOfwmOPPYYHH3wQX//611Esrk3l+9a3voXNmzfDNE1EIhE88cQTeM973uPnlDsIA94usUGS+0gMoAncxAc9u0ljr7R3rClbuY/EgdyW6782YGQ3uD7qcpmM8pKZuYwNtXlsiYZv7UjwDOKqhLRcg0rY7jAnapgX0ihzMVAErYfdI26x6/xFf+qXWsl9GpkY58tx20XC6VVgmqgtkmmWp5dKHuWeD4XcxzLu/VQOILbVqNVAAYgQXIRG99wEAHirsBX//OJZ/N1LF9uye11Nua96GlgF3XjMAZvPN+uq2LJ7jfrpu2/pTtuVRVKI4LFbB/BrN/dhz0BwXcB396dxhzqHe6aOAWUy38euOIe7R/J4784ebJBc3vlutOX4XmFEURR+7/d+D7/1W7+Ff/zHf8S3v/1tlMtlfP7zn1/1se644w6cP38eCwsL6OrqAk2vm55bHbSDiACwgpUNHwS5b9iWBxKqPQAkCkBuBFg8D0y9BTREgFtDd9XaHFAes8b9e8ksRNpAMp9F2lQhSFVk5v0nu4Ysw7AL65lCuJYcAHiEr6A6aqVz0bUqkPY5acmDl4wMZofuBGWa2E+Q3Od3bgXz/cPQaQbzU/7UTaiXL6MWscl9OoPselHusylg2iI2tSKZ+4xeKkOhbeU+LFuOpx+ELJON4jQajWZBLUmrg7DH6u+xxCexOLWA5C4VYmNtRdGmaaJxzlLu2e5uMKkUZFVvFpUmQ7SRUZEImFwO+sICIktuszVZ1X2ztzmFxADAZrvAsTQ25YK3qWzvTYJnREgVK+rT1DRQLLnmYc6zBHh3RmES+80mk0k88cQTeOKJJ9o+Vj5k720HBCCkgVpA5N5JyyFF7gFg4wGL3BsaMPk6MLyGRmuTb7jjgdv8m5tPoGkaH9HG0BgbBcXzME3TV8XL22yIzYX/nWdSLpnXKxVEBgaInUtWLRWRZyjQBB940R07kFFq0CkaqenLvnyGjUuXUeG6AIpCtCuDaAjq9dWQ6MoAsMn9EiFyXy6jwVifFy0I4RTUxqJWAaqqQpXIKveGpx8DQzCBRNi2FVQkgoQqYnbWWoSWJXVN5F5fWIBu99hximm9jauSQjjdaR2w3QXoCwtglxYB0wQoCnLDvzhMzRuDmQs3ApROuc9gvVoFmyUXd9xSx/QuVO47UngH4cDx3ZMm96bp2nL8jsH0YtPd7vjyy2s7xjon9wAQ6bWsOaaiQC/5u7W6MDWLy8kezEaz0HLhJeU4YDxKvU64OVDDLqLjCBNjtqsLvyaO4tFLr+Cuoz9p+3imaUK6PIZ6JAo6mUQmKYRmcbgSiYIbpVovk2m6ppdKruc+JFuOEKHxscWj+NiZ53H/xJHl39AGTl6cw8t9u/F693bUEuSazFEcB37nTiQbEvRSCWZDQXmNjayuasmR1w+5jxQs3z2vys3iYV9tOUVvd9pwyT2TdO+pRtV/a6es6lB1a2HkXYh2lPsOOggKDrlXKoChk7OgqBJg2ioISeV+k6d4/NJLazuGtwnWeiX3/X3NsTY97avyMjq+iBcHrISgD6Z7MOTbkdeG/5+99w6TozzTfu8K3dVdnacnamY0QRoFFBCSDBgQIhiD/Bl2SYsN5pxds4tlHHe9LP6812JsbJawnF3shWNsvmPv4rDGGJtgYRNMEFFISAKU00gzo8mdQ3XF80d1V1VLE7uru2uk+l0XFzXTVaWa7uq37vd5n+d+yKLIfeUmoYqiaA4WjKPyQ7J70WKkR8cgxWIQR0bhaCq9qFYaH0eSl6EQBOhgEEG3NfLtAYBpCKMpEwEly2jIVi5yH8ilIVAOeP1sVQsUCxAEAdbLIgcFUqKyk9DjY0kcDqgrWGd6KjieAnAvXwbv4ZcAAOLIKOLZeSWdp9gGsyDu9YlCrd2dCkW17ckRnNFEILCkHXUe875HRd1pQ3UYT+WQ5EQwDhJ1HmfV7DAVRQHnCyDC+EDLUkUcc57/aBC9Yxk4KAKX5utPCKcTpNM641K1sCP3NrWhqEttBbsqFjnlVC5nGqEOINihbve9q04qZoMkAv3vqdveZiBQOSeKcqCb9aJafnDQ1HMnI4butOHKRQVnyjATxOvzzsRL7WtwZKRy96ggSpALaTnOyot7xtAFPLd/X1nn4o8c0YtpA0EELZJvDwBUuB6XHduKS/rfx8c4c+/VAlI8hjWj+3H50S24+WOtIMnarFoUJqIKx0HmK9d0zdhJ2VWhTsra+ZevgDc/joojIyVH7rndu7VtJt/91kqR+4K49wlZtGUjmBd0m7oCJI0bIvfhOuweTOB32wfwP1v6MJKobI2GkUiax6/IDmzq+jg+rO+uSJfaQndfUVZAJdUJ/emYkgPY4t6mVhTZYVamch7ACeK+wl/yrnXq/yUe6Jul3/3wR3rhb8fHLdWZ1shIqAXPd5yDJxeux85DZTbsOoFkzCDu62vfHj7LenHM34QhTxiRWGWs2wCAM3QVZZjKi2PXEl3cZ/eWJ+65AwfQnB7Hpw+/iU8v8GJxc2WjubOBrtfTcqTxsSn2LB1jahoVrN2E1FgfIlcwes9l9Jx+Y/OsSuBesRyevLgXyhD32Y92qRsOB5jFiwAUR+5rWVALALRh5UwcMXdMBQDJGLmvq9MKiYHqdagFAA9Dg2TUCHqlutQWegQwNAXkC2ptcW9jU02K7DAruJScM5y7kmk5ANC1Xt8+/Orsjj32jr49/7zJ96sxVEMDxt0BcDSD2Fh0+gNmQdogcv3NtS+oLWoOlK6cuM8abEUZV+WXj5nFi/Fmy3I803U+/md/eQ9Y/uBBUFAQ5NNYvGIh6r3mdywtFTqsi3tjsbaZFAo1gWKBXW16/U3Y1rAIbzcvQzZaufE0ZyjYZSrcGMjZ3Q3GxcAl8hCHhxHPzF7cS6k0+MOHAQCuRYu09Iwg60Rr0A2fi4a3hCJdMzF63Ysj5nf+LiqorasDLxl97qtXI8LQJCiXOj5kKaYykft8eqPLQULK59yfjvn2gJ1zb1MrqtXIiq9wd1ojRnF/6GXgE9+e+bHHDEW4863bhTlo9LqPmDs4J5NqlI6SJbCGYsha4TYU1GYr2BwoZyjWrYq47+pCzB1AwulBdjwOUZJBl9gwJ7f/5GJFq0AGAoDDAQgCxPEKift85J70+0FUsMnZdBxz12NPuBMAkInGUKmRjsuqaRyULMFZQZ97ACAoCu6VK+E7nsZoyonEWBS8KMM5i7qG3N49qqkCdHtNADi3O4xzu2s/xgB6l1oZBI4NxxEbToKmSHTVmyNKCwW1pMcD0umsWYdagiDgZRkkAHA0Y3rkXpIVbVXCRSiAkG9SdpqKeztyb1MbqiXuK92d1oivCWheqW4P7lR962eCLANHXle3XQGgaVllrs8E6ubrRW2xhLle96mMKhxYMQe6ofZuOWzI4B9eweZAWWNajrvykW/C6UR9SH3gCbE4xmOlfY6KomidP+nGRlCB6jXEmQkEQeBo+xI813Uefsl048iY+b0ZEikOT3efjz8tOB/v9ZbWrNEMXKxL285W0Nkpx6n5/A5ZqqjPfQF2zRr4BHXVTBgcRCw7u3qC7Ecfaduu5dYcV42R+z8k3Xjug0G8e9i8yaiYF/dUfiWrIIAJorriHgA8+fs0RzkgmJw+xhkchhhZX+Wx03JsbKpJLcS9swr5wAs/oW8ffHlmxwztBLL5pdOuCy3XvMqI0+MBm8/TTKTMi2bzoqzl83ocpCXcDTwh/R7NchUsUkzqKT9ut2uKPc2jaV5+8iTLGNxzsKRzSGNj4GNxfFC/AP2LV2MsVb3ivBkTDCHGeJHlJaRnKQynQ5FlpDM5JJ0ejPvCRXnc1Yb16sWt6Xjluinr4l4AVQXRxK5ZjfmJYawaPYALhz+atbMNV8i3B+A2RO6tBFVXB1AUSCigUqrgNcsKUxEEyPnUsYKzWU5Uxb2DIqtuW+v1qfepQhBImxwcKhL3ov5dt8W9jU01ORUj9wDQc5m+vW/TzI4x5ud3X2TixVQGv0eNLqezAgTeHEGT4gTNl9jrrawLx0xxB/V7tJKdP7uIDD677yVcd+BVLAtVp7ivuadD2x7cc6ikc+QOHkTa4cYH9Qvw5rwVePdw7SLXk8EG8g92RUEqYm7hvpxIgCfVzFaiRg2sCrg8ekfsbAUKFYH8Sk3+++6QRJDeyo+n7jPPRBsXxfLxI2je/uasXWS4fOSecDotlzZWgCBJbaWSTqj3qFnivqjgO+9xr9nu1sC21WuoY0qlzK1jMr5nTkEfr6uxwmRFbHFvUxtcBmeJU8UtBwDazwHYfC7nwZdnZolpjPB3X1yZ6zKRgD8/WCoKYoPmFIAlxiJqehIAn5+dZu/qQDloOPOCLZerXFRWSqVAKTJcEg+XvzoPonkrdMec4d6Bks6RO3AAcUa9XqquDiEL2WAW8Ab1uonkmLnjjBSPaw2sSJcLTA0787KG+yabrEzxNy/JkAvivkppOaTHA9fSpQCA3IGDEKMzL+KXkknwvb0AAGbpEhAO9bPaP5zEY5sP44n3+nB4tDLNzWZLwTHHmYgCsoScIEOWlbLPW1xMm4/c59NyanG/egL6MzidNLeOqShyL+hplNVYYbIitri3qQ2nauSepIDFG9RtIT29a04mAhx9U92u61b/szgBQy56tM8c//DcyBjcYg6EosAXso6dIuNUH4AcL06zZ+nIKX15mvJV52+vX7kMDll9GI4MlmYTmTtwEPH8hJkO1yFsIaecAmMoF2UAACAASURBVF5Dv4RU1NxxRopGwZP57rQMA1cVbQVPxG10djI5IlpAUYCF3Bjak8NoykarJprYNXpDv+z2mXfg5Xbp/vbuZXq+fSIrIMmJGIhlIZogoM2gUFTrlATIGfXz48Tyo/dSRM/dp+vCECVZ+5trEbn3BQ3iPmPuamiW112AnLwxcm+Lexub6nEquuUUWHKlvv3hb6bed/+f9A66Sz5tWX97I4F6XTBFj5sTuW/lorj24Gv47L6XsKLRGpF7AHDlfec5QYIsy9PsXRpySr9Hq/Ugojwe1AfU9Kd4LFWSIMwdOIBYXtxToTqEvbWvkzgRX73eQTkdMzddRYxG9ci92w13LSP3AX1sy2Yq4+zkclA4P3oI6wd2YuXYoardq+41qyEQFCKMD7u3fDDj47I7d2rbrmV6vn2iyOPeGoaBdIMq7hlJT0/khPLHm0IxLaDbYDoo9RlTk7Qcjwug1fc8Y3KqY0+TFzeeMx9Xn9WKNkUfz0j29EzLscadbXP6UZPIfZV8qBdcArjrgGwE2PsH1cffNcm/vfv3+vaST1fn+sok1Kh70MdGzMmzFoaHAQAkFLibm0w5pxl00wLqIsfAyGpErRKCZl9cwLGGHjgkAU1M9R5ETa2NGNzbD8gyBnbuxsLz18742IJTTqx+BUifDxTDIMRaT9y768MgFRkyQSJlcgGfFImCN4h7M7uKzhbWkH6UNTkiakSbiFIUCFd1ir/ZNWvwSvtqjLAh0EfSOFOQZvReZ7ZuNZxjtbZtbIY12wLdSlFwzGEkAVI6DRrm5N1LhrQcui4E1knjy5f0QJIViBUKVkxFex2LayIfwTE4AHejub1MXA5Kuy+iXAqFJ78dubexqSY1ccup0pecdgLLr1G3RQ7Y9dTE+yWHgQMvqtv+VqBt5uKqlrR0NGH1yD6sG9iJznhp+donYmzeYrSGqzWr3Tw+NrIXK8cOQUlWplDxSFrBrnAXdjQugsRWr5i4ZWG7tt2/a3aOOeLgIIRUGgnGA6quDnUeByjSeqtOjvp6uPLOGWmT01WkaESL3BMuV43FvaH4O1s521YprXf9rJbTCh0Oo65enbyIIyOIDE2fRqaIIrLvv68e39AAR4deQB7LN8Ny0iRYpzWcyYziXs43zONMEPeioTMzVa+LaYokqtrAqgBDUwiyDjgUCVKFxlMA2uoHYBfU2thUF4cLoPI5uqdSzn2BVTfp2+8+qjVSKeKD/wGU/AB+5mctbYFpJNQ+D2dEjqIjOQx2sM+Uc4rDRnFvncg9ZWhkJZnsy1zAWKxrbJxVaRauWoI1w3tx2dH30HVo5/QHGOB270bSyUImSNDhetR5rJdvDwB0fRguSRX32TQHZaLvYYmI0RMj9zXMuQ940cjF0ZYcQV2iMg27AL0+pNpFik1n5AvAFQXH331/2v25PXs1gcd+bK02EZFlBYmsWj8TcDuqbgU5Gbq457XrzvImiPsxXdzT9bXvHQIAlE8d45RMBopQGaMCyZDqaBfU2thUm0L0viringCcVZzBt64G2s5Wt0d2qx1rjYi8KvoLrLqxetdWJnRTE5DvxikcP27KOV+MUnizZTk+DHfD0WSdyD3pM4j7eGXuU97goc8EqjcBbVx5BpamBtGUjULc9eGsjs3u2qUX0zY2WDLfHgBoQ+RezGZNyWMuUJyW46pJJLQA46CwIbYPFw3swBkjpfUtmI6PBuL4VeNq/G7BOvTVtVXk35iMptUrte3h7dPfq+l33ta23Wv1FdEkJ0LOT/ACbmuk5ADQxjxGEoB0yrQVBWnUIO4bzE2DKRXSr49xRhFeLnsGE9g3lERfJFNkUnC6puXYOfc2tcMVANIj1RH3jK/6xarnfhF4cou6/dJ3gO5LADI/n975SyCRT2lZdAUQXlDdaysDgqbhaG6GMDAAYaB8ca8oCg5nASEwDyE+pfkxWwHK74cCQCQo8PEEKjE95Aze4XQVW6WTTidcixaB27UL/KHDqiXnDB+EnFHcNzSg3qLingwE0JMaQltqBH5nAjRl3hggRaNYOt6LNieLpp7GmqclkQE/pFisYukO2UwOvEKAd7hBsNWVDi1nnwX8n6cBScLwR/ugyDIIcvLYZPr1zdq29/zztW1jh9ughaxbC5H79uQIlo5sQ8f620w5rxa5pyhQwSD6IhnsH06CoSksavai0VedugkjR33NOB7uRo5yYkEiAYRC0x80A944MIZUToTPReNTNTApsBp25N6mdhQi97kEIJvTtOMkCm451UzJKXDGXwLN+YjT0AfA2z9Ut5PDwIvf1vdb943qX1uZyG3zEWF86JUYZMq0GMzwEsS8w4fXzYCgrJOedMARxC8XX4ZfL74U+4Yq44mdy+fWOmmy6n+7+8z8/akoyO6YWWqOoijgdu0GK2TRomThqw8hbNG0HIIg0MVIWBTrR+vgQTgo8x55YjSC+akRLIv04oKVHdMfUGEovzqeyokElAoUS3JpvWaBYav7eYdCXjDz5wMAopw05b0qpVLIbN8OAHB0zIfTkG9vLKa1UuSe9PtBMAxIKJBGzXEgA3RxT4fDIEgSwwkOH/TH8V5vBPFMbToq72Mb8UHDQuyrm49s1JxUR0VRkMmnMbmdFOS0Qdx7bHFvY1NdjEW1ucrkMxdF7qsNSQKX36P//NJdwDNfBX72Kb1x1/Jrgfazq39tZfLBvKXY1PVxvN62CsOHy8u7T6U5KHlvZ5/PGt1pCzh8Xij5FZ+siUvIRnhBzQF2Oqq/kEqvWoMRdxB7QvNxYMvMxL04NARpfBwL48dxVVjCF9YvsFQU9ETosNpUTopETRW9BScSMhAAQdd+EVzrkaAoRQWFZpFN6ud0u6sb8XVQJMKL1B4gCcaDxAsvTrpv+o03AFH9TnnXXVj0WswgaINu66w2EQShRe+FkVFTzqnIMsRxtf6CzhfT5kT9/q9VGpnHrU8MUzFzVu1zoqylW7FOqijdh6ziaqiVsMW9Te0wivtsBbrUyrIeua+WU86JdK0D1v2juq3IwPv/BYznc2J9LcCGB2pzXWUSbNCXUqP95TWyMna59QWsFWVhDc2BuAo0B5JlBUI+cs/UwLkje8YKvNBxNrY1LcFHe/tndEzmfb2g0b3qTACwTGHiRFD1+Y7RkgQpZt44I+W7pdImpRWUy5ZgF57uvgBP9FyMlMndeAEgl9b98xlP9XtRzFu5FCBJiCSNwRdehiJNvNqb2PS8tu296KKi11a0BrBhRTPOWxC2XJ1IQdzL8TjkbPm9CqR4XJvkUA0FcW/o4lqjAnCvxyjuzQmYZAzFx24HpefcEwTIKjqQWQlb3NvUjkpH7mvRwGoiLv7nfOqNQQCFe4C/2QR4wjW7rHIINhu87gfLizQlhvWiL7+FutMCABs0NAdKmW8xmOMFzTGCcVY/+tvS1Qbap05ghgbHofD8NEcA2fe3a9vus86q2LWZBRFuQNLhxqgrgNH+YVPOKfM8hHQGMacHuXAjJAt0OhVYL5JOFjzlQMakiKgRztAci/FVX9w3NIa0FJvxeAbpt94+aR8plULqtdcAAFQ4DM+55xS9HvI4saTZj3O6w/AwtV9tMeJobgYAbG/owR/e2o8/7y3vXhVH9XG5ELk3FpTXookVAHi9uthOJswR98aeAG4nra1ckR7PlLUZpzKn519tYw0qHbmvlQ3miZAkcOmdwFffB67+MfC5p4Db3gbqumt3TWVS19qsbUfLbGRlbITlr7dGFLSA29AcKJMxX9xnDQ83hql+JJGmSDTOUy3y4iSDmEG4T0Zm+/sQCAoKQcC9alWlL7FskqFGPL1gHf7UeQ7eP2iOuJeiMcQYL57rPh+/aTwLr+4zL0+6VNysniqTiZkfLOEM9z/rrb64r/M4wSxZDJeYg0DSiD3xxEn7JP6wCUpObeLlv/xyS6RLzRRHizqmHvG3YM+xMRwaKS+1SprABtMYua9VXwafYTU0lTBnNTTLi9o266S0ZmunazEtYIt7m1riDurblXDMsYq4L1DXDZx5A7DwUoCybo7yTKjr1K3wYpHyhERyXP/s/Q3WWslg6/R7lOPMF/dKOoV5qTE0ZGMIuWrzsG1bok4yFYJA7xvvTbmvlEojt3cftjUtxlPnXoff748jwdWmMG+meBt096VkzBwnGWMDK9LthruGDawKuDx6RDRrUkTUSC6rimZCUeCoQZHigkYPvnrTRbghvgudySEkX3oJucOHtdcVRUHkv/9b+zlwzTVVv8ZyoJtUcc9IAqRUGllBKqsvQ7HH/cmRe6eJxeWzwRfQc+CTJq2GnpyWUxD3p2e+PWCLe5taUukutVZJyzkF8bc2g4b6oIjGy4swJQxRxmCLNRqtFCju/Gm+iPUIHC7pfx+XH92Ctez0KTGVoOts3UO898P9U+6b2bIFkGWMuwJQWuejL5qBq4b+7jPB26RPGNNmiftIBDlKXWkhXC64LdDp1G2IpldS3DslAZSv+uKeoSmwXjfCf/3X6i8UBaP/8ZD2emLTJvCHDgEA3GvXwL18WdHxSU7AwZEUxlI5CJL5bkLlUojcOyUBcioFSVbAl3Gd4gQe9wVnLsZBgqyRdavPsBqaMmk11CjuXRQg5w0aqNPUKQewxb1NLam0uDfm8dvi3lRImkYg7zaRSOUgl5FznMgvzVKyBG+LdRpYAQDldMCZ90bP5cwX38ZmK5S3NvdoR1er1ltgYDAKMTJ5mlX6rbcgEiRijBfO+W0Ie5xw1ih3d6a4GhvhlNSJWdok0StGo+CMkXsLiHvWr0cpMwnz3XK4fLM1hyzWNCIavOEzoPJFzMkXXkD86achDA5i+N57tX3q/+7vTjruWCSDZ3cex+NvH8WHAxXsrVIidHMLAMAl8ZCT6n1aTtO1iSL3BbecWjZc84cN4j5rzphqzLl3SXoQ5nR1ygFscW9TSyou7g1Rulq55ZzChAJqpFASBMTHoiWfZ8H4UfRE+9CVGNSKyqyEK1/omhXEafacPUV+zDXKD/W7aQQ72wEAY4wfiT+/Oum+6TffxLg7AIUk4WhtQ5O/+k1wZgvd2AhGUkVEOlW+Cwmg2mAWRe4tkJbjNkTTs2nznZ0uJCI47/iHWDV6ULfdrAGU14Omb31L+/n4Hd/EwYsv0bqxei+9FN716086zuhxH7SQx30BR3MTADVyL6UL4r70/i8nintFUbTJgqtGTjkA4AoFQcvqWJrKmbMa6iBJ+Fw0KJKAi9e/43bOvY1NLXAZiie5U7ig9hQlWKdHYMZ6Z2ajOBGLez/EOcN7cF66r6aiYTIYpyoEcnx5ObATIRu6idbqQUQQBDpXLQEACBSN3j9vnnA/vrcX/JEjGHGHQDc3g3A60Rqyvs0c3dAAl6iK+1yGg2hCSoYUjVou595tcHbi0uZMYoy0c1F0JwbRmRyq2b06EMvi5T3D+FPzSmSv/9xJrzvmz0fznXdOeGwkrUeJQ6y1bDABgKqrA+FwgMmn5QDlinvdLYeqb4CsAIubveiq92BesHbfW8rrRWMmiqZ0BE2p8swYClzQU4+/XdeNr1yyEF5RT/U5ncX93Ckltzn1qHjk3s65ryThxjpgdz9cIo/M4BCAldMecyKKJEEYGgIAOFpbTb5Cc/g4EUXm2IdqLizHgXKb92DcOZzB213nwSGLuMLpRa3KiReetRTbWRZyJoPDuw7jzHgcVCBQtE/ij38CAIywQTBdXQCA1hqKhJmiim81jiWl08gIEvxlFhOKUT3nnnS74bJAWo4nYLBtzeRMP7+cqn3Xz2iaxwf96rNi4d9+EY2dLYj++teQ4nF4L7gAjXf8ExyNE6f2RfPiniIJS3WnLUCQJOjmZjApXdxnyxD3Bbccwu0G6WFBEASuWN5iyrWWA+Fw4NLIPiiZDJzMAnPPTRBQDA3cTueCWlvc29SOaqblMP7J97MpiTM6GxDY/zKcsoSm2LklnUMcGdEarVhV3M/z0khl1AiTnEiaKu6TKQ5xRhVKClu7B1FH2ItgTzcCr7+AUHIc8eeeQ91NNxXtk3j+ecggMOoOwr9wAbwMbUmRNBEeLwvk1EK7LC/B7yrvuqVIFDlaPYdl0nIMzk7ZrPniXipKIavNvVrv1RsgjacFrL7l8wjf8vlpj5NlBdF8d9oQ66hZMel0OJqawMQHoORyUAS+vJz7fJoSXV9vuSZzlM8HMZOBnDCnwN2IVFTHdPpG7u20HJvaYRfUzmnY9lY4ZTWyxPcPlHSOxNF+8KQqjByt80y7NjOh/Pp9KifMvU+5rL6E7K5hlCnAOnDbZ9fh/MGPMC8zjthvnixKQcru2IHcvn0YdQeBllaQPj9aQ27LiYbJ8BSKTQUByWj5gkKKRsHlI/eMl4WjRraCRjxBP9YM78PHBz/CstgxU8/NCRIGMjIijA8c5aiZaAp59EnZeGrmE5hYVtAajdUZOqRaDbqlBUy+IFROpZHlS4vcKzyvdWMuFNNaCcqvPo+lpPni3gorTFag9iOSzekL7QQcefu2U7mJ1SmKo61d2+aPHS3pHG/uHcQTiy7FEz0XI9lozcg95ddXfaSEuc2BOEOElfHVdgnZvWQJXCtWAABye/ci9cqr2mvj//VfAIDj3nq4lqkWg53hubPk7cunrBCKgrTBIrBUpEJaDkUVNY+qJTTjxLLcKBbEj6M+OmTquQfjHJ6n5mFT18exP9wJwsTVq9nA0BSCrCrwx2bh0hVJ698z4wTBajiam+ERspifGMYSKotGf2kTEaPjlRXFPelTx1Qlm51RV+yp4AQJT27rx6YPB/FBf6zYpOA0dsux03JsaosrAAgZO3I/B6EbG0CwLJRMBkJvaeI+Nqq67PCUA7622ueDTkTWF0C/px4C5YBvLA4ze3PmOP3B5g7U/h6t/8Kt6P/yVwAAI//Pg/B8/Fxwu3cjmc+3T4abwCzqAUEAnfXV71JaKosaWIT++BQYkUeXdHIh5mwRozFcGemF1NqO9lXWWXEi/X7ImQxkkyehOVHSRBjDOGq6YtPgYxDLCBAkBdEMj7B3egE8ntK/Z2ErR+6bmxDk07jw+E60IIJgQ2mR54k87ncfT+CNg6NgaArnLwxjYWPtxpsxfyNe6zgHWYcLn9w3iLNXdJR8rnRORF9EdYciCQKtRpOCGvRjsAq2uLepLa4AkBy0c+7nIARBYGDhShwbSSBJeHFLhoNnllHMWD5FgpIlBNqtGbnvcwbxavtqAEDdWAJt0+w/G4rEvb/2DyLvpZeCXHEm9vRF0H2oF0euu1611Mun6Fx/zTqQFy7EUIID65w7jw9PYz3Sohq9FUdHp9l7ahRZhhSNgpElMD4XGn3WiNwD6iqTODRk+gpTTpAhC2q6iIupbeS70efCgWE1OjuSzM1I3I8Zxb3Xek45BRwteoBDGBos+TzFTjmquM8KItI5CemchFr38CJ8XkTc6kUkouXdq8YGVqyTKu4dYkH3tWphp+XY1JZC3r2QBiSTO4DaaTkVZ7x1AQ4G2zDsCmLsSN+sjlUUBfF8sx2vkIWzzUzZbB5uQ7pMNmmufziXb4xFyRIcJ7jT1IJdxxN4/q/+Hu+1r8RRfxP4Q4cgx9WJt/vMMxG66SYEWSeWNM+tyTLdoHc+Llfcy4kEIKmCgg7VlXUusxECIcScXgyRLISsOd0/AYCXZC1y73LVVhw3+HQxP5qcWd49TRFwOSjQJIE6C9pgFjD2+RCHSk+tMt7jdLjQnVZX9LX0uQcAn09f9UsmyhtT07zef8TD0JawF7YCcyf0YnNq4tIdHsDFAY+J+YEFcU+71Px+G9Opaw4DR9QHydiRfnQs65nxsRleAp9Uxb2PlEH6rSkYWUNEPWtSE6QCufyDyalIID21T3Op9zJQgiH4r7gCBzdxWBA/DgKAe+0atD30EAiq9q4wpVAk7kdGyjqXMZ+50NnXKrwVWoiD3eokcVUkhmCrOU3hOI7XXK0Yd21XKhoN4n44MbMJzOXLmqEoCjK8ZFmnHACgDeJeGBqGJCugSrhe48Sg0ByLE/UIdy071AKA128U9+V1U07n9L/Lw1DFrk6ncUGtLe5tasuJjjmmivv8cp8dta8Y4dZmAHsBAON9s1tGjmdyWpQlEPBY1nnFmAvPmdz5M5dfUmZo0hJ/f5OfQYOPwej8+VA+vxEi9VfoaQ0h2dEDeObuBJlubMCBQCtSThauMRHXlHEucXQMMacX/b4GNITa4U7zqLPIe+NmdeGbicZNE/fZpC7AXJ7ainsPQ8PnopHkRIwk1aLamQh2giDgYawteahQCATD4MXG5YiJzah77RC+dPHCWZ9HGNTFPd2spvpYKXLP+P1wSgJ4yoFUurwVpnTOELl30pCTurin7Jx7G5sa4TZE7s12zClE7m1xXzHqu/Q8+bHB8VkdGz0+CsjqAydQV/uUlMlgQ/q1ZTPmpTrIsgJeyEdDndZw8CAIAud2h/HszuMgnE5scbWBbWrES9sHQJMEzukKY3mr3xITkdlANzRib10H4owX7gyJqxWl5L9BHBvFmDuAHQ098NBNqItmLCnu0zHz8u65lD6pdVlghWllWxCiLKMlYP0marOBIAjQzU2QFBK5VAa8KEGUZNCztFo15us7WtQJnpUi95TfB1bMqeI+k4NSxvexSNwzNFJGK0w7LcfGpkYURe6j5p1XUWxxXwUae7pAKAoUgsD4+OyKoiP9enQp1GCt9AYjbEhPF+JMbA4kKwrOObYDOYWAv906jisLGjxoC7nRH80iyYl4dudx7bWjkTRWtFl3IjYZlNcDNwXEAfAZDrwklyxwpLExcJQ6GSM9rCUaWBUwCu9sPDXFnrMjaxT33toL6rO7Zj5elCMca4GztRWuoxkoggCFy5XUUVnMR+4JlgWZLyo1Ru4ZuraRe9Lnh1vkEGO8ELkcOEGGu8Quz2m+OC0nnjLk3J/GVph2Qa1NbTkx594sRA6Q8zN62ymnYjgb6uEn1Pd5PJGdse80AESHdLu2ULP1vJgLsCFD50+uPE9mI6TAY+H4USyL9GKx0+Ri8jIgCAKfWtEC/wndZ8NeJz6xtKlGV1U+3ryTk5xOI5MrrTkQAIhjY6rHPQCSdZcsSioBaxDe2TJzmY1whtQJ9xxLdfjVlj78Zmsf3j40u5XFWuFobdUaWUmJBLhZNrJSFAXC8LB6ruZmbWLDCep5nDRZ87oDyu8DK6iBEjnHI2WIvs+WQuTeSZNwUqTmlkOy7JytETIDW9zb1JZKpeXYTjlVgSAIhPPFUUIqjfgsnA8io/pKTV27ObnBlYByMXDkH4a5nHnivsjVwWKWbR6Gxmc+1o61nSF0hFmc01WHv1rbDpeFotSzxZt36FB4HqlE6VFtcXQMOTofuWetFbk3OjtlkuZF7nMZtZDcIYmgLXavTgUnSBhOcOiPZtE7bt5kp5I4WtvgEtVxRkokkBVmJ+7leBxKNv95GQp0c6Iaua911B5QI/ceUZ0wKnwOSa704EbBLYd1UiAIQhtXrTamVpvaf8qnMLt27QJJqoVyJ/73wx/+sNaXZw2K0nJscT8XaWjIf4aKgqHDM297f2nsIP7XkbdwYf8O+CzqcV+gIOC4MiK+JyJZvPDLw9BY19OAa1a34byF9XNa2AOAN6i/x4nh0rvUiqOjhsi9x1qRe78+1mXT5jk7XR/I4q/2v4wrj7xpmcZAiqJgPJXDzr5YUd61kaG4vuLQHLBOP4KpcLS1gZFUcS8nk0U+7jNBMDjl0Pl8e0VRNHFvhe+xGrlX708ll0OSKy1yrygK1swP4cz2ABY3qfe+nM+5P53z7QE7576i3HPPPVCUk9MUmpqa8Ld/+7c1uCILUqm0HLs7bdWob6kHDqliaeTIAJauXjqj4+T+YwjlUgjlUmDarelxX8DF0MhmONAZ8yL32VgcCQcLpyzC77VTxyqNNxQAoNpYquJ+UUnnEcfGwFFBgCRBulxw1bg40YgnUBnbViWdhFOW4JQlUD5r3Kvv9Ubx5kF13HFQJM6Yd/J1DcT092DeHCm+dba1wiWVHrkXBg3FtE165P6K5c3ICTIcdO3rD0ifDw3ZGNYM70N9M43O+tJy4wmCwDndYe1nRZIgZ9TVY8oW9zaV4NChQ3jjjTewe/fuk4p5/H4/3O65MdBUHDstZ87T0tGCjk2vIpBLoXH8+PQH5OF7ewEAVDAIKhiceucac5U0AO7ANgCAzPMgneW7oxwcjOOZBRcAAC5zhWHttYu5j6++DsARAEBytPTifXFsDLlAA0iWBeOgap6/bMQd1AVuxkRnJ6kohcwaoqnFEIk/FslMKO6Pjutpgm2hufHMdbS1gRH1nPvsLCP3Yj7fHtAj9wRBYFGTdZ6DlM+HAJ9BgD8KdyyEgNsctzA5rade2ZF7m4pw77334stf/jKWLp1ZFLMSKIqCvr4+9PX1IZkfnC3nGiBwQNNt6nasHvjTn8w5b3pUP+/YPPPOa3MSEiGift0SAMABMYVjM3ivFUlCZv2FANQozpH8MYWVLp/Ph/b2drS3t1vinqUNDbbkRAJkffkFwEbPfJe39vaCpzqBFv0zS45FpthzchRRhBSJIFfnzOfbWyuz1WOwlOVMLP4u9g63hkhsCbjgpEnwoowjY+mT/O6zvISRpDrBafAxlve4L0CFw5oPvVyCuDd63DvyHvdWg6BpkCwLOZMpmjiWi2y0wbTIfVor5sbdPsfo7+/Hr371K2zbtq1m1yDLMjZv3gyPx4Ply5fD77eoN7UsAUPz1W2nD6iffcOOCcmMA7Fl6ra/DfA2TL2/TckokgRuiSruSZYF09097THZVAbZ7h5QsgyX3wumTY9bK4qCRCKB/fv348iRI1i3bh1IsrYiijKIeymRAG2CuC9qDGSL+4rjn6c7/SSjpXnAi5EIJBAQKBpOlgXrtNYj1BUM4C8ObQYjCQgE1phyzlROxJsJEkL9AtRnY+jyWkM00RSJzrAH+4eT4AQJA7Es2uv00YpykQAAIABJREFU79HRSBqFrNiO8Nz5fhEEAV+TmmoiJZPI5GZXbCpO4HFvRUi/H3ImAzlRej8GTpBAEICTUmsbjXVMpPf0tcEE7ILaivDAAw8gnU5jyZIlWLBgAf7lX/4Fg4Oz695ZLocOHUI4HMbatWsRCASsKewBgDDcgkrpdlgnIeueviCtkxN7KkJQFAiHuqyqcLkJ60xOhMsJyNIMUk435BNSXAiCQCAQwMc+9jGEw2EcOnSoItc9G8iAQdzHzakNKbYXPL0fRNXA19qCcDaO1tQo6qLD0x8wAdLYGCSCRFMmghDrhN9tLXFPsix8igCnLEJOmtPEKsWJ+DDH4IP6Bej3Nliq+HtBo/69OTBSHAHeN6T/3BmeW98vf0sTzjv+IS7ufQ/nhGb37C7uTquK+wwvYjCeRSTNgxflyQ6tKpTPB45yYIQHDo4kZ2WjXGBrbxSPvHIID79yEAOxLOS0YYXJIpPQWmGLe5MZGRnBT37yE+3nw4cP43vf+x4WL16MRx55pGrXcfjwYSxevLhq/17JEARA5MW3bJ4TCRTDuQhb3FcaglE7Y4qKAomfPtIkCvpEzsFMnr++ePFiHD58uPwLLJMhNox3ms/A6/POxMCwObUhXEYv9nP5rSOYTlUcjY3Y0PceLu7fjhUDu0s6hzg2Bqcs4rJjW/HZZhlXLLdW2gNBENoqkxw3R9znRAkKr3qSOyXRUukOnWEPHJQqfvcMJjXhmuFF9I6paW8+Fz1n8u0LuNra0J0YRGt6DL7I7CaiwrAq7kmvVysqPTKWxv9s6cN/vdWLPYPmdS4uBzLgxzvNy7Cp5Sw8836/Zmk5Gwr++IKkwEWTxfbCp3nOvS3uTSadTuPhhx/Gv/7rv+KGG25AIKDmQCaTSXzpS1/C7bffXpXrUBQFDoc1WtpPC5mPfpkp7o3nqnFKx+mAwLgRcfkRZ7zITtPFVVEUiPlW6KQsT1mc6nA4ZrQSUGlSbh8OBttwzN+E8ag5/uHGbrdswDqC6VSFoGnQjY0Aiu0CZ4M4Oqpt0w3WbLxWEPdm5TLnRBlKvr+DUxYt1fXT5aCwuFn9e3lR1oRrLCNorjCLm33WXbmeBEeb7h4mDAzM+DhFUSAO5RtYGVJyOGN3WovUiVA+P9iC132OK8kOM2OYEHgYGlLKTsspYK01xVOArq4udHV1aT+nUil85zvfwUMPPQRBEPBv//ZvWL16NT772c9Oeo7BwcGiNJ5U/obdsWMHvIbZaEtLC1paJo4czanBjKQACWq0XVHUaH652JH7qkIzTiCtilVhmkZPkqxAFtVBmVZkLeo/GVa4l11+/UFhzJUvB57Txb3LFvdVwdHcDHFoCNL4eEmuR+Ko7o9PmVB3UQkG6+bheJIETznQzglgXeUFeXKCDIVXv9OMywHCYsGSlW0BfDSgpsq9fXgcCxq9mBd04/Pnd2FrbxTLJnDRsToOQw2S0N8/4+OkaBRKTh1X6CZjAyv9eWgV61bK74NHUN2rlBxfkrgv9DegSQIMTSJrwcLvWmGtb6kFuOuuuyZsOjWT/3rz1n5GvF4vHnjgATz99NNaJP1b3/rWlNHIRx99FGvWrNH+W79+PQBg/fr1Rb9/9NFHK/IeVB2j+FZMit4XRe6tMZidyjhcui2dIEw9SIuSDOQfNjRNWk4sTATrN/qHz7wL71RwWVUwEYoCV2DuCZC5CG3o2FlK9F4c08W9GUXVlaAvOA87GxZiT10HEpHy60NyogQ5L+5drvItYM2mye/C4mZVyImSrEVzXQ4KF/TUI+Sx3jVPh7OtDUmHG4NsHfb0RWacJ1/kcV8Uudefh1ZpukYGAvAIauReznFIzbJwGABS+aaCHoZWu9Macu5Jz+mdlmNH7k/gH/7hH0puMDVZFB0ANmzYgB/+8IfYuHEjent7sX37dqxevXrCfb/whS/gqquu0n5OpVJYv349XnvttZMi96cERvEtS3qaTjnYkfuqQroYUIoMiSAhihJkRQE5ScRdjeyrk1t6jqSOuQ3imzOpORCXr01wyGKRG49N5TjS0Im3u85Dhnbh+gP9WDZ//qyOF8fGsLN+AQY89eiM0PhERkCAtdY97HbrYjYVjQPzypuEqGk5ajTY5bZml9cLFzVgIJrFBT31aPRZ8xpng6OtDR/Wd+NwoBWOuANLOQH13qlXOAFAHDq5mBYAsrw+ObBCh1oAoPwBeMR8l1ouh8QsI/eiJGuTFg+j/k1W7MdQK2xxfwJ+vx/+Cj1ob731Vjz00EPYs2cPDh06NKm4PzHdJpG3ilq1alXFrq2mGMW8WXn3tltOVSFIEg6KhCQDEEWIogSnY+LhhTdEaByMtYTRZLBBfYk3a1JzoFx+SZkhFJDTpCbZmAMZDiPOqA/9+PDYNHufjDg2irjTi4g7AIIngdpnjJ2E26MXj2Zi5RdPZrM5QFLHZRdrzcJUL0Pj5o93WEa4lgvl88GdHxtn08iK7+vTth2temqPscut2yLvERUIgBUKOfe5WaflpA3vSaGHgZzSUyZP9w611l8PP4UgCAJXX301AMydYtdqQFYgLadwHoIsOYd/ZGQEHR0duPzyy825pjnCrbfeioaGBuzatWtWxzmc+j3NT1JUqygKBEEV94SigHbNDVHrDhqbA01dMDxTrux7F1cdegMXJ2pv9Xm6EGjUo9jxkdk3spJGx5ClnSCcThC0Ax6LpDgYYQ0CPJMovz4kZ0hDc3usGxU/VYR9AW9DHQBATiaRTs9stVDo0/PznYZVqYK4p0lCcxeqNVTAD7fIg1AUyFwOSW52aTkFpxzAIO6LIvd2zr1NFeno6ACAuWFTWS2K0nJM8rovrACUkZKTyWQwOjqKPkM05HSgv78f0WgU8Vn6uTsN+bj8JAJYUhTIhQeNLIF0WVcsGGHrgiDydTJcxhxx70zE4BcyqHedWqLEygRbG7XtUvLRxbExcDSjNmtzkKAp6z1CWb/esCmTKN/ZiTNEQxm72VrV8LUYmq71HZ/RMXzfMW3b6LjD5aPcLgdlCYMCQI3ck1DAilxJkXvjZMDvUsW9ZOfca1hvZDrFiUajWL58OZYuXVrrS7EOxAk592ZQiNzPoljz7bffLvq5s7MT/f39Ne00XAuefvppHD9+HOedd96sjnO4XZoA5nlx4qJxBWByGVCyBJpQQMzSraRW0KwbzvytlC2h8OtEFFnWokyne4SpmgTb5mnbidjshK+cyUBOp5GlnSBZFl7GmlmtbpOLv0MQ0JSOoI5L2J2Uq4ivVc+ZT/bNzA6zELknXC7QDWpXdkVRtMi9y0IrTWQ+xZgVOMg5DllegiDNvMFWyjAZ8OUdoeQit5zTW9xbc3Q6hdm0aRO+//3v1/oyrIXZOfeKAij5QWKGkftIJILvf//7eO6554p+X1dXV/71zDEcDgcaGxun3/EESLcbtCJDIChIggBJVkCfsARMSiLYnBoJJL1ey0SRZoKLoZHjJFPEvZzJqPcp7IdQNWGb6kFDhggSyeTshK8wNAyepCCSNBivF6zTmo9Pj1+fLGZMEPfneEW09G0FADgvW1H2+Wxmhn9+KwA1NTIxODLt/ookabaZzvY2bWwVJAVSvvurVfLtAYAKBAEAXiGLRC4HL0Mjk5MQYGcWkFvZFkRnvQdJTkSjT03vlAs+9xQFwm3N+pBqYc3RaQ6zdetWRCIRXHbZZScJl1/84hdYu3ZtkROODU7IuTchLUeZvQ3mV7/6Va2fgE1pECQJp4OCIAGkIEASRdBUcWReyeq5o+QcG3w7KB7J+DBccRmyLIMsw8IzPhbDrrpOOCUB8331mJ1ni02pkDQNr8uBGCchmZ5depU4PASOUkUE6fFYMt8eANg6vT4ka4KzU5EDid9eZaoWgU59VEjNoPhbHB6Gkq9ncrTrxzppEl+7tAecKGki3wpQ+TqmNSP7cUmsHt0X3jar4500iXovU+QiVBD3cy1wVAlscW8ikiRh3bp14DgOl1xyCe6//36sXr0asVgMjz32GADgwQcfrPFVWhCz03Lk2dlg3n333fjFL36h9ROwKR232wl6PAJKkUFzWYApFvdyRo8kzjVxf64jjczgRwAAJZMFyuiAODoaw/bGRQAAwlOPVaZcoc1M8PtYxLgkeF5ANpWBe4apJsLgELJ0Xtx7fWAtmpbjCQe1bTOcnWS7MVBN8Hd3atup0fFp9+eP6bVhzvb2otdIkrDcSlPhXnJJPMh4zJRzFjrUUhbqolwr7Jx7E6EoCj//+c+xevVqbNmyBevXr8fatWtx33334eqrr8btt99e60u0Jqan5czcBvPBBx/Eb37zGwDAtm3bsHbtWqxduxaHDh3CK6+8ghtvvBFLlizR9v/Nb36DL3zhC1iyZAn+5m/+BmNjY/jOd76D9evXw+v1YsOGDejv70c6ncb3vvc9bNiwAX6/H5/4xCeKug4XGB0dxVe+8hVcdNFFaG1tRU9PD+6//37IBitPRVFw//33Y+3atVi8eDEoSi2K+tnPflZ0LkEQcP/99+PSSy/FkiVL0NLSgo0bNyIajWr7xGIxfPe739XSje6//36EQiHccsstAIBjx47hrrvuQnt7O1599dWTrvd3v/sdNmzYgLVr1yIUCuGqq64qctWhWRYHDx3EX2zciHPWr0coFAJBEOjs7IQoyRDSBnHPzq38XSqoiyYpVt7DiDMIJqN1oU3l8Ru6DcdnWKgI5CP3tDpZpbzWjdw7gkE0ZGNozERRl45Of8A0yClD5N5ri/tq4fJ74cg7H6XGY1M2vgQAvveItu3osP5aIOFwgMyLcClRvmUrUBy5P92xxb3JXHvttdi2bRuSySRSqRS2bduGe++9FwsXLqz1pVkXs91yZhG5/8Y3voFnnnkGALBmzRps3boVW7duxTvvvIOHH34Yv/rVr8BxevTrqquuwrJly7Bv3z7s2rULTz75JO644w689tpruOOOO/DHP/4RN9xwA+6++27ceuuteP755/Haa6/hlVdewa233lr0bw8ODmLDhg24+eab8eqrr+Lo0aPYsGED7rjjDnzxi1/U9vvBD36A5557Dps3b8a+ffvw7rvvIhQKFZ1LkiRcffXVCIVCePnll7F37178+7//O3784x/joosuQi6Xw29/+1usXr0a3/72txGNRvHkk0/innvuQSwWw09/+lN8+OGHePjhh/HAAw+gf4KW5/feey82bdqE3/3ud9i6dSs2b96Md955B+eddx727dsHAEjLMq7auBEbb7wRb/z61xgaGtImDqkMjygciDFeKG4WBG2tSNJ0UAY7zHLFfdZgUeiysL3gqYg/rH+OkVmI++LIvXVz7qlgEJcf3YJPHnsPZ0fKs1nN8CIeP05gU+e52FG/0K4PqSIEQcBbFwSpyCCyGYijo1Punzt8WNtmuhdU+vJMgcw3B5Rm6cwmSDK2HIlgz2ACI8m8Vz7Pa83WbJMCOy3n9OXR9UBq+iKdqiHz+Q0CIGfYA8DbCHzhtZN/X5RzX9r89aabbsKGDRvw29/+tuj3DMNoNqadnZ3YuHGj9tqtt96KO++8E4cPH8brr78OilInFmeddRbOOOMM/PGPfwTHcXDl7R+//OUv48Ybb8TZZ58NAKBpGnfffTd++MMf4ic/+Qluv/12LFy4EL/85S9x9tlnw51PY1m7du1Jq0CPPPIISJLE3/3d32m/+8xnPoP77rsPO3bswM9//nPccsstuOKKK9DY2IhMJoM333wT4+PjePTRRyGKIlasWIH77rsPe/bswbPPPlt0/h07duDBBx/E4OAg6LwoX758OW666Sb8x3/8B773ve/h8ccfx5tvv42+wUGcvXIlFEEAKcn4wQ9+gAsuuABcNj8Ig4DDM7ei9oAeuVcACNEoyom3c4YVDNccfC/mMgvbwpCe3gVWzCE4fv6MjxOGh9CYiWLN8F6El7Sg0W/NHg2k2w2CYaDkcuVPQnkJSU5E1uVHMJe0RVOVuaGNQvKll0AAyB04AMcURgf8IYO4X9CtbR8bz+BoJA23g8LCRi+CrHUcyqhAEOLxQewiAziyawiipOB/rWyZ9rh4VsCbB9U6hKUtflyxvBlSWg+YkGWkTJ4q2OL+dCU1AiRnHrWaU8wy534yAoHAhL935u0b2RPSShry1mMMw2jCvkBdXR1EUUQkEsG8efMwMjKC3//+99izZw+eeOKJon1b850F9+7di4ULF0KWZfzsZz/DlVdeiU9+8pMAgBtuuAGvv/66dsyjjz6KWCyGc889t+hciUQCra2tOJyP6ng8HtTV1SGTyeCb3/wmKIrCbbcVFzJN9Hc/9thjyOVyuOCCCyY8/8CAatVWSCf60nfvxoP3PghPPIOGxhA+ecWnIOejKowkgPSGT35jLc4RdxgvLLwIOcqBTw/Ece70h0yK0aLQ5bMfRNWktasVcly9X+njJ69QTYY4NIy6XBJ1MoclZ3WBKKOgutJQgQDEkZHy08dEGXJODbyo31s7cl9NfD0LUEjgyx04AO/5k09GC5F70ucDVa83a+uPZbC1V03Pqvcy1hL3eTvMw2wDiKNjIJ1OiFLTtP0j4lndsSzgLthg6uljlJ0+Zov70xbv7K0OK4osQI2JzjJyPxEluOVMxIkCvcBkVfhTuacUjuF59UG5bds2yLKMBx98EBs2bJjyOr7+9a/jc5/7HC6//HJcddVVuPPOO7FmzRp0d6vRmXQ6jd27d+P222/HfffdN+O/q6mpacrXjbz33ntYunQp3nnnnSnPfdlll2HZGWfgmRdfwNvbL8FtX7gN//eXvoqv/9O3II2pRWEuQtZyLecStM+n5Vxn4slp9p6aXFpP9XLbqQ5VxdHWqm0LAzMX98LQkHp8U5OlhT2grjKJIyOzTnc4kSwvQeH1SbldUFtdmEU92nbuwIFJ95PTaYj5mi6mu7voGcUJ+vPQbbE6ESofSPIJWSRzOSgOJxKciDrP1BOQCcW9we3OnoTa4v70ZaJ0lloyug8Q8tHMllVAOTZWJkXuK0kkEgEA9Pb2TrvvTTfdBL/fj6997Wt45pln8Mwzz+Dmm2/Gj370I7Asi1hMLbaaybnKud7EDIqenE4nNr/xBv5+40Y8/uST+M73v4Mf/38/wb/9279j/dnngpF4OAP+OWlTxgb92nYmmZ5iz+kppCgBgCtgP4iqiaO1VR1fFAV8/8yaA8npNOS8UKabJ54UW4kDDV34KBcCTznwfw1H0doUmv6gCeAECUo+IOGUBJA+/zRH2JgJs0DPnc/tn1zc5470atvOBcX59lleN2dwWcjnHjCIez6DOJcD6fUhluFnJ+5ZVdxLdgOrIqwdfrA5fTDTMUc2J3JfSbz5yMKmTZsmfD2VSuHQIb0Y7sorr8S+ffvwyCOPoL6+Ho8//jhuvvnmonO9/PLLyOUm9u7esWNH2dc7MjIyabde4/lDoRD+z2OP4d3fP41PXvoJDA4ex1//9eew68MPwIo5UKHShEat8Rr8w8ttDpTN8tq2J2BHQ6sJ6XSCa2nHsDuEI+Mz+xz5fNpZhPFBaO2AbCG/8IkQvAFE3AGknCxS46Wn5uREXdwzigTSrg+pKmMiiR3L12HzvJU40jeq+difSG7fXm2bOVHcGyP3lhP36mTRz6e1YthYdvomgYkJI/dGVydb3Nvi3sYamNnIyqS0nEqyapXqbP6HP/xhQsvJhx9+WLM++8d//EcAaufYL37xi/jggw/Q3t6Op556CvF4HIFAAJ2dnRgfH58wLefdd9+dVJTP9nq/+c1vFtl0AkAymcTjjz8OAHjyySfxzjvvgPR6sXLlCjz10EP41tf+HoIg4Nmnfg1HOAySsWYh4nR46gz+4eny/MOz+cg9oShwByeu7bCpHC/3nI8XOz6GzWw75PT0qzBC/wBkEHi+81w84VuK/3mvb9pjaonRuz8dLd1mkBNkTXS5GMecXHGby8SzAg50rcRRfzPGSRdyhyZ2P+J279G2XcvOKHqtIO5pkoCDstbnR2ppORnIeVe6eGZ6cV+I3NMkoVnSSnH9Pif99gqTLe5trEGlIvczSMspFMiKYvGkQpLU85woZgv7FV4vUBDjJ+4/0WsdHR249NJLoSgKrr76ajzxxBOQJAmSJOG///u/8fLLL2v2qX/+859x8OBB7VwtLS24/vrrQZKkludfsJu866678N3vflfrtvvOO+9g48aN+Mu//MuTruXEv3eqv/vzn/88AOCll17C9ddfr1llHjt2DFdffTU+/elPa/v++Mc/BkEQcMybB9rF4GufvQEA4PR4QE/h9mB1PPV12namzOZA3kwCgVwKPj4Deo6uZMxl/EF1tYSnHEgdnV6oC/39yNJOKAQB0u+Hh7Fm0KAA6zeI+1g54l6CnI/cu91zc1I+l2EZWhszOZoB99FHE+7H7d6tbbsMfVkAgOPV8dztpCw3OaP8hbScrCFyz091CBRF0SYAAVafcMpJ/T4vnPd0xhb3NtbATK/7WablNDY2wu/348CBA8jlcnjvvfewfft27Ny5E4DaaGp8XO8QWIiC7969u0gAv//++9r+IyO6zWg2m9XEubHh06OPPoqmpibEYjHccMMN8Pl88Hg8+MpXvoKHH35Y24/neVx77bXYv38/ADVl57XXXsPNN98MX77A7fbbb8f5558PRVHw7W9/G6FQCD6fD+eddx7uuOMOhMOqO83IyAjGxlQLsRdffPGk90KSJO0a9+zRo0Hr1q3TVhCeeuopzJ8/H8FgEB0dHejp6cHFF1+s7fvTn/4UDzzwABSKgrO7Gy/u3g23243Pf+Urli9EnApnMAA6vyqU5aZ+AE3HuuHduPLIW7iq71071aEGBBr0CVX00NFp9xcG+pGhVQtbyu+Dz2XtcjWvIdUrHU9NsefUZHhJE11u1hb31cbjpEA3qi5sHO1EdgJxr0gSuHyfEUdra1GzPUVRtMi91fLtAT3nnpF40LwaMIlNE7lP5USI+bS4QkoOUBy5p/x2quPcfdLanFoQJkbuldlF7mmaxk9/+lOIooiLL74Y+/fvx7PPPouLLroIAMBxHJYsWYJNmzbhuuuuwz//8z8DUMV8Z2cnNm/ejG984xtYt26dtn9PTw8eeOAB/Pa3v0VHRweOH1dtR6+77jp8/etfBwAsWLAAW7ZswU033aQ1pbrwwguxefNm9PT0FF3jBx98gKVLl2LJkiVYv349rrnmGvzkJz/RXmcYBi+88AL+9//+32hvbwdJkuju7saTTz6Jz3zmMwCAH/3oR+jp6UEmo+YZX3XVVbjhhhu0c+zYsQPd3d3Yvn07ANWlx+ib/8ADD+Cxxx7DihUr4HA4EAgEcPfdd+M///M/T3pP/+mf/gnhcBhnn302fv7EE3jrrbewbNmyaT8LK0OQpJazmuWmXzqeioKLCRUIWC6adjoQamnQtiPHpi+q5QcGkHEYxf0MHb1qhCdoEPeJ0ou/uUwWyAcwWLtIseq4nRToevVezVIMsh98cNI+/NGjUPJjuuuMpcWvSTKkvBC2Wr49oOfcEwB8OfU+TWRFiNLJq98FImk9sBIy2Hoau9xSdlqO7ZZjYxEqEbknqBm77lxzzTW45pprin535513nrTfpz71qQmPX7duHR588MEJX7v22msn/Xfnz5+Pn//851Ne20eTLMWeCMuyuOeee3DPPfdM+PrGjRuLmm6dyKpVq3D06NRRzFtuuUVLAZqI6667bto26XMZF+NAUhTA8SIURSlZmBvFvU31CbXrjXIi/UPT7i/0D6iRe5IE6fXCy1j70ekNGZydyij+XhWkQA/vBU85wLR3T3+Ajak4KRJONwOqvh5cLoXcnm2Q4vGicSNjqKdyLVtedDxnYaccoHj883MpZAHIioJoRkCDb+KVIpIgML+ORSRd7KpjTMsh7bQcW9zbWAQzc+4LkXuLFtPazF1YNwOkBciCgFw2BxfrmvU5FEHQPJltcV8bwp3zte3o8PgUe6qpDUJ/P9Jsq+rCQZCWT8vx1AVBKAoUgkA6M7GD1kxoJ3MQo8cAAHTwLLMuz2aGEAQB1knD2dqK7PAgoCjIbN0K36WXavtktrynbbP5budGuhs8yPLStPaStcA4/rWkxxBq9qHex8DlmDyppL2ORXudmspoDCQVpeUE7Mi9tUcom9MHs9xyFKU4cm9jYyJnOjl09O8AIwsgkgmgBHE/PDSOP3acDack4IxQGzrNv0ybaQiF/SBYFkomg9j41I2epEgEciqFdMCliQarp+XQwSAYSQBHO5EpQ9xLMf29sR1IaoOHoeBobUV2505IIJDZskUT94qiIPOeKu4Jtxvu5cWpjwHWgb9Y1XrSOa0CaRD3C+IDmL+iZYq9T8a4clqUlmM3W7Nz7m0sQlFaThmRe0WB2ukWduTexnTaAwzmp0bQlIkCidK6f8bHYhhzB3Hc24C0z3bKqQU+Fw1HXlgkOQFScvKOw/yRIwCADO0CFQiBIGD5tBwqGIRLUnOTMxxfcqqcFNc98qlAcIo9bSoF66ThmNcCEAQ42onkq69qnyff2wsx3zmZPessEE7rReengvR4gHxHdONEshSk/HhMuN1z7n2oBLa4t7EGRQW15UTurd+d1mbuYnSikGKlNQfKGKwJ3V532ddkM3toioQvn5eecrDgDx+edF8+3/k57XCBCgXhcdKgSGsXQVOBABZHjmLN8D58PDqxN/p0iJKMkfEE0rQLIkHaKWQ1wsNQIBgXHPPmgaMZCEePadaXieef1/c7/7xaXWLJEAShFb9KM+iALsvKpBNVOaFO0O1iWhVb3NtYA7Mi93OgO63N3MUo7sVSxX1cjxK7fZ6yr8mmNIJNYRCKAloWkTlwcNL9ckeOQAIBnnKACgbhtXi+PQAQTicWC1EsjR5F52hvSYXfsayAJ/ok/G7hhXivaakt7mtEk9+FRU0+rD5zARySGvhKPPcHKIqCxLPPafv5JzF7sDqF+6pgMiCjnXJCAAAgAElEQVRIMo7HssiJJ+uAvmgGj7x6CE9s7cPBkeLVtsLkwBb3KtYfpWxODwhCjbQrknmRe1vc25iM5A9i1B1AjnLCORpDKY+RrMGakPXb9oK14i/WtGPo/30JJBSIBxZMuh9/pBcUFNyw72U0X/SPUBqbqniVpUMFg5AzmZJXmDhB0rqGOiUBlN1JuSYsmxfAsnkBiO2X4MAP7gIAxH79azi7u7SUMXbtWjhaTs5Xf2XfCI6OpcEyNK5Y3gy/BWtFyHwdi5xMYuvhMbx5OApZUfCXZ7Wiq744+DGcyIEXZQxEs1g+T78fZZ6Hkr9X7doQFTtyb2MdCo45ZkXu7bQcG5MZcwfwp45z8GrbWdg3WprFYDapNxVibVeHmhFYsghkvj4nd+DApPsV0nJoxolwV/ukFn1Wo7DKJMXjJeXcc4IEhVOLcRlZsCOiNYYOhRD6q+sBAHImg6F/0a2aQzfdOOEx8YyAaEbAQDQL2qKpZMYVIVbkIOfv1aH4yV3AR5L67xr9+vdQjuv5+vZ9qmKLexvrUIi0K1K+MLYEjFF/O3JvYzIeg394tkT/8Ewqq22zQdvVoVZQ4bAmgHMHJ07LkXke/DHVCtLZ2TmnOiwTwQAyNIMIzSIxPvvoPSfIkHlV3DslwU7LsQD1t90G8gQnGPacc+C74ooJ90/z6vOQJAhLNrEC1ElLgXpJF+9DiexJ+44k1PvRQRGoMzawMhTE2+JeZe6MVDanPmZ43dtpOTYVhA3pOfdGkT4bsmmDuK+zBVOtIAgCTL4TtDgyAjEaPWkf/uBBQFQFkmvJ/9/enYdHVZ1/AP/Olpkkk3WyEhJ2wg5KENCyCAX3pbhQbLVQsaiItbZWXNrSH1iriBWqdTegtmoLFhFQFCqiorIJKAHCEggkIStZZt/O74/J3MxkJslMCJmF7+d58jDMvXdyJmfuzDvnvuc9+d3avnNVltwD7/efhI19xuPA8cqgj3eN3LuCLbXDBjmr5YSUEAJISUXeykLE9HelkWmnTkXOs8vanFNhsro+D2Nj5GG7ErYiuSW4jzc2IC7G9bl9psECp7NlkM9gsaPB5FoZPD1BDbnHlQiHx8i9nFdDATDnnsKJvFXFHEUnXp5eE2r58qaupU1r+SDq7OJAJo/j4lIZMIWKEALfDyhAeZkFKqcduQeKoP3RZV77mA8eBAAcTOmF2NwRqCtrwODsxLCvlgMAcQnxAFwjmoazbZf6bIvZ5oTT0hzcwwl5fFxXNo8C5HAKrNx+AkaLHZlJGtxaMBR9162D02iCQtv2hHwhBIzNwX1cTPh+Fio8Ru6d9fXokZaNo1V6mG0OVDVZkJXkWkvk1NmWK6U5yd6vRadXjXsG9wBH7imceFXM6eSkWs/jmHNPXSw2LRXK5teY0dTJ4N7sqj8e47BBlczgPlRkMhmOpffBycQslGnTYf5+v88+5oOHAADHknrg+/hs/O9QFcI/rHeJT26ZrK1v0Lezp3+eOfexcZqwHfmNdgq5DBa7A3angMHieu+RyeXtBvaA68uZo3nkO14dvp+FnsG94+xZ9Na1PK8TtS3FB07VtVzxzEv1Du4djR5pORy5B8DgnsJJV6TlsBQmnUeymBgpd9VosnbqMQbXl2JobQkG1J/2yZ+l7pXepycAwKRUo/H7Az7bzYcOQgDQx8RCkaZDokbplQ4QzrTJLUGOocnQzp7+me0OCEtzcK/lqH0oJTQvmqY32wOeHG20tgx0xarCeeTeo7zw2bPI07W81k42B/dCCJTWuUbulXIZeiR7rwzuaORKyq0xuKfw0TotpzOYlkPnWVxzOTmz2SqNjAVjQNVxXFR9BAXWqoiaoBmN0nKzIVO7qm6cOXTMK3ASdjssRQdhVKrhTEyGXBOL5LjIWfkyLjUFsubnY+zE5G+TyQphdX2BjU9gcB9K8c3Bvd0pYLY5AzrGnZLjOj58B7qUXiP39UiKVUGndZ1n5fVmnDVYUd5gRmNzvn1OSiyUCu/3Ta+0nETOYwIY3FM48RxpF50M7gXTcuj8io9zBYPCZuvUiKh78pecdcNDLi1BDWVGBgCg1mCFrbkyDgCYi4rgNBrREKOFKjsLAJAaHznBvVKng8bhCs4NnQju3V8IFE4HYpjqEFJadctAld4S2GejZ3DvnqQajlqn5QDAkOyW19v3ZQ2obrJA3pwWNjjb97XoaPAM7nk1FGBwT+GkS0fuZQBHRek8iPdIUdBX1QZ1rHA4pFEmBauPhFxavBqqHFdqToNaC/1XX0nbjDt2NN8fD1WPHACRFtynQm13BfdGgznoWvcz8jT4ydFtuPLkt8xjDjHPVZEDDe4NHmk5kTKhVgrue7gmraclqDGiZxJG5SZj7oQ+mDgwDQMyfBf+czS1BPdMy3Fh9EPhoytz7pmSQ+eJO0UhxmGDqda3fGJ7LPUNMMlVcEDGuuFhQKeNQUyeK7ivV2th+Gq7tM2wcycAV9Cvyukh7R8pFKmp0si93WyGxR5YOoeb0tCIeLsZKRY9v4iGmNfIvTmw4L63Lh7ThmTi0n46ZCZqOj4gRDzfB93BfVyMElcMzcLMglwpFS5ercToXqk+KTlAq7Qcvq8CYHBP4cQzIHec48h9GE+mtVgsePPNNzF27FgsWrQo6ONffvllzJgxA8nJybjooovw4Ycfdn0jqU0FaTGYdehT3HrkM2RaGjo+wMOpshqsGTAZ7wyahv1JeeephRSouBgFtD2yIIuNRb06AcZvvoHTbIZDr4fxW9fIfVNqpjS6mBJBOffKlBQpuHcaTVLN80B51g5nwBRanUnLSY2PwbCcJIztqwvrK04ypRLy5teXvb5lsCQ/KwExysBCVO+0HI7cAwzuKZyca8690wnA6ftYYcTpdGLx4sVYsmQJdjRf9g/G/fffj5MnT+L999/H9u3bcfToUdxwww2deizqHHVaKhRwpTjYa+uCOtZY3/IhpGEFkpCTyWRI06oR06sXTEo1jGYbGj/+GE2bPoEwmyEAmIeOBCBDgkYJTZiu8umPLCYGsc3pGE6TCSYbg/tI1ZngPpIomucfOc4Gv5IyADjcI/cqFWSa8L1K0Z0Y3FP4kMldP0Dncu49V6eVhWdajlwux5IlS3DvvfcGfeyOHTvw97//HTfeeCMAYMiQIXj99dfRs2dPaIJ8Q6uoqMCJEyeCbgMBylSddNtRF1zOvdGj3nhsQvt1qql7ZCRqoBkyBABQo0lE3cpVqHv7bQCAXhULWf5gAECaVh2yNnbWSFkTbjj2BWYe/ATZSYG/R9QbrfimzIDDybmoUycw5z7EvHPubSFsyfmhbF6l1tnYCGEL/vlJ85gSE7keQzMG9xRe3Kk5ncm59/xCEKYj926Jnbh0uHr1agBAssfCR7feeitKS0sxYsSIoB5rxYoVDO47SalLlW4HPXLvEdzHJTK4Dwe9UuMwcnQ+fqQxIsXSBMuhQ7A0r0yr6dsHYy8ZhJ4pseiZEhvilgYvISURCTYTFI0NQQVNNXordtU5sTNrMMq06Ry5D7FYlQLThmTipot7YsqgzA73F0LgVJ0RdQYrLPZOzl/rRl6TahuCS3UEWkbumZLTIjyHN+nCJVcCDqsrUBcCCOZbeAQtYCXvRCWf4uJiAIBKpTqn371v3z6sWLECV1xxxTk9zoVKpOjwXVp/mJVqpNXacX0Qxxo9SmfGJbFkWzjonRaP3mnxMD50N07+4puWDQoF+i96HHEDM0LXuHOkSPWuRCLP7DgwBACT1QGnxQwA0NgtDO5DTCaTYVhO4H1gsTuxevdpAK7VXG8a3fN8Na1LtK6Yo0xLC/hY4XDAqXcNmjC4b8HgnsJL64o5iiBeoiK6F7BqaB7ROJfLjqWlpbjxxhthNAZf95pc1GmpKNL1gZDJYDAEV4HEczGh+GR+EIWTuNGjkbP0aVS/8ALkMWqk3HE74i66KNTNOideKWS1tVAFGNwbrXYId3DvsEoTHikyeOble6b0hCvP4N5+9iyCSYBzNjVJt1kGswXTcii8nEut+06m5Rw5cgS//OUvMW3aNFgsFtx2221ISkrC66+/Lu1z4sQJzJkzBxMnTkRGRgaGDx+ON954w+ex1qxZgwkTJmD8+PHIzs7GxIkTsXnz5uCeRytz585FQUEBdu3aBQC4/vrrUVBQgLlz52LIkCFQKBSQyWSYPXu2dMztt98OtVoNmUyGyZMnA3AF9nPnzkVT85vhvHnzUFBQgIcffjioxwEAu92OVatWIT8/H1u3bsWnn36K3NxcFBQUoNGjLNmrr76KadOmYeTIkdDpdLjttttw6tSpc/p7hJoiKQma5gnfRpM1qGMNBrN0Oz6VAVO4SbzqKvRbvx593l+D5Oa5LZFMlpqKI0k52J/WD7uPVgV8nNHmgDBbALiCe47cRxaDZ3CvjoTgviXVNNhJtY5GVsrxh8E9hZcuC+4De0NbtGgRCgoKUFhYCJvNht/97nfYsGEDGhsbUVhYCAA4cOAAbr31VjzyyCPYtm0bTpw4gfz8fNx555148sknpccqLCzEzTffjMWLF+Prr7/G7t27cezYMVx//fUoKysL7rl4eO2117Br1y6MHj0aALBu3Trs2rULr732GoqKirBs2TKfY9566y188MEHXvfl5eXhk08+wbXXXgvAVVJz165deOqpp4J6nKKiIkycOBGzZ89GcXExSkpKMGfOHJw+fRq7d++WKvfcfffdKCsrw8cff4x9+/ZhzZo1WLt2LcaPH4/q6upO/z1CTSaTIU7tSo0ymq1BLQ5kNJgAAHLhRFxaagd7U3ey2p04Vq3HsWrXJf6zBiuqmyxBL/4UTlQpqdiVOQj70/qh6ExTxwc0c6XlNAf3diuDpjBgsjpQWmvED2UNqNVb2t23yaMWfnwEBPdKPwtZBcqrDCYnfkvCv9fpvCi56WbYa2pC3QxfwtFc0hKu0XdZ298/lWlp6LNmdcsdnQzuJ02ahClTpuDo0aO4+eabcerUKTz11FOYNGkSAOCOO+7AY489hoEDBwIA4uLi8Ic//AFr1qzB4sWLsWDBAmi1WvzjH/8AAGmEu0ePHrj++uvx0ksv4dtvv8WMGTMC/CMEZ+TIkX7vz8/PPy+PM2TIEGzfvh2TJk3Ctm3b8NZbb2H//v3YtWsXNm3ahAkTJuDDDz/E9u3bsX//fum4yZMn44orrsDatWuxYsUKLF68OKj2hZO4ODVqGyywmy0w2xxSycGOGI3uPGZrUHmldH6ZbQ68uu047E6BzEQN+qVrsaf0LPafbkCCRokbRuUgPSHyquUo01IRa7dAHxOHpsbAU/FMVgeE2fVajdXEQKZkqBBqJTUGbDpwBgAwKT8dunaqN3kG9wkRlpbjqA82uG+ZgMu0nBbh3+t0XthramCvrAx1M7pWJyfU5uW5FhNKTEzE/PnzIZPJ8MQTTwAAdu3ahT179mDx4sV4+umnpWMcDgdyclxL0h89ehSjRo3C5MmT0aNHD6/HTk9PBwDo9XqcL23l4Aebmx/s47j/bvPmzUNqaiqmT5+O6dOnA3BdFaioqMC4ceO8jjl79ixycnJw8uTJoNoWbuLjNECDBXA6oa+tR2x2x4G6EAJGs6tiiQYOyONZLSdcaFQKpGpjUNVoQWWjGafPGnG0ynXOmqwOJMWe2yT2UFHqdFJwbzSaYXc4/a7w2ZrR5oDTbIZcOKFhydaw4BmkN3WwSm2juaUyUqIm/F+73hNqg0zL8Rjp97wCcKFjcH+BCttRQ+H0WGVWDsjaDtJ9noPnyH0Qde4VCtfvyMjI8AlkdzYvQf+vf/0LgwcPbvdxli5dKt3esWMHXnvtNXz22WeupjmDm3gZCdx/t0w/k/R27tyJKVOm4L333uvuZnWL+IRYoMI1YtRUVYv0AIJ7ALju2Bcw1OuhzEhnPeYwMzwnCVsaXXnp/9l1Wrq/X4Y24JUyw40iJRVxdlcKh9NkgsHiQFJcx8/FYLZBmM2ItVuhTGX6WDjwDNIbTe2XNfXcnhgb/mGeIvkcRu7rW74MKBjcS8K/1+m88EpnCSdWA1DjKvmI+DQgKTfwYzuRltORujpXHfMTJ050GNwDwPbt27Fw4UIMGTIEDz/8MHr06IE///nPXdKWSFJXVxfVdfS1iVrptr42wA8jIRBXU4k4ux1qbY+O96duNSQ7Ed8cr4XB0nIFUCYDxvaJ3OBWqXOl5QCAMJmgt9qRFNf+SK7DKaSSrbF2CxS6yH3+0USrUUIuk8EpBBo6Cu6bR/Y1KgXUyvAuCw14T6i1B5tz77E/g/sWkTkcQdHLMyh3BDuhtvlDWSZ3jfp3Aa3WFcRt3LjR7/aysjLUNM9deOmllzBlyhQ8+uijeOmll9CvX78uaUMk0mq12LNnD86cOeN3+969e7u5RV0rPtkzuA/sMrKzsRGwu17TniUKKTwoFXJMHZwJpbzlikpBr9R2c5vDnSI5GbEOV0Un18h9x++pNocTuSoHUsxNSLLqoUxhcB8OFHKZNArfYLK1OdHb6RTQNwf3kZBvDzRXuWn+zA46LcdjpF/hscDjhY7BPYWXrqiW04U17keNGgUAeP3113Ho0CGf7UuXLkVsbCwqKyuxYMECTJ06FVdeeWWX/f5AxMTEAHDls3tyOBxe/3bX4wCuv5vdbsejjz7qs620tBTr168P+LHCkTa15UNEX9/Yzp4t7HUeH0KpHGEKR/3StfjpJXkY0zsVN13cEz8aEKbpiwGSKZXQNqdzOE0mr/rnbdGoFLg61Y5rTnyNSysOQMG0nLCR3HzVxWp3wmj1/35stDngnlaRGCFzRWQKhVRuNehqOUzL8YvBPYUXmRxA88hZMMG9EC37t5On7/9Q1wiI3e77+yZMmIABAwbAZDJh2rRp+PTTTwEAVqsVS5cuRUNDA+Lj43Hy5EnY7XZYrd51zy3N5eTcOffu39WZgNn9WO5/3bKysgAA27Ztk2rI79+/H7/5zW8AuIJpz5x/dxDf+vkG+zjt/d3uvPNOAK7yoPfeey9qa2sBuMpo3nzzzbjlllsCft7hKCUtBT301ehXX4bkpsA+jCrKq3E4ORcnEzJhTY7soDGapSeo8aMBacjTxYW6KV1Cm+B6HoGO3AOAw+OLqJJfRMNGcmyMdLu+jdQcrVqJ+Zf3x92T+uHy/PTuato5c4+6Bxvce6bxcOS+BYN7Ci8yWcvIe1DBfedXpy0qKgIA/PDDD6ioqPDaJpfL8eabb0Kr1eL06dOYPn06tFot4uPjsXz5cjzzzDMAgEGDBiExMRFbtmzBihUrsGPHDvzmN7+RasTv3LkTzzzzjJTD705LCTQ95dSpU1I7P//8c69tffr0QX5+Purr69G/f3/k5eXhmmuuwe9+9zsArqB8zJgx+OabbwAA/fv3l54vALzyyitBP47T6ZSuZLi/8Hj62c9+JgXwL774IjIyMpCUlIShQ4fipptuCrpMZ7hJz07DlNPfYfyZA8ht8p961NrxsjrszBqML3JGoi6RwT11j4TE5i8pNhsaAyyH6ThbJ91WMC0nbHjOl6g3tr2AnkwmQ2yMAgkRUCnHzX2FyGkwSGssBEJK41GpWIHMA4N7Cj8Kj+A+0AVknJ0L7ufMmYOf/OQnAICGhgbk5+fj2Wef9dpn3Lhx+Prrr3HdddchISEBcrkcN9xwA7766ivodK7c6cTERLzzzjvo168f/vjHP+Lxxx/HrFmz8NJLLyExMRE7d+7E5MmTodPpMGnSJLzwwgsAgPXr16Nv3744duxYm2188MEHMXDgQGnl13vuucdrhVyZTIZ///vfuPjii6FQKDB48GBs27YNeXl5yMrKwvLly7F9+3apLOWCBQswffp0LFq0CPfddx+uu+66oB5HpVKhZ8+e0mJVTz/9tHR1w00mk+Gdd96RtimVSmRlZeHFF1/Eww8/HHD/hCtlWkvOvCPA9SL09S3lULWprMdM3SMhJRFJFj2yDLVIcZg7PgCAvdYjuOfIfdhIiWsZuW8wtj+pNtJ4VmUK9D0VaEnLUSYnswKZh8iYbUEXFrkKQHOg6LQDigBGH7wq5QSellNYWCitRNueYcOGYd26de3uc/XVV+Pqq6/2ub/BY5ENwHfkvSPPPvuszxeO1kaMGIHdu3f73N/6SgQAxMbGYtOmTZ1+nNGjR6O8vLyjZkOhUOChhx7CQw891OG+kcazDKu9KrDVdpsaW4L7BFYgoW4Sp0vBdSUfAwB6x7Y92uv2xZFq7KuUA7kXY+yZIpbCDCPJsSoo5DIkxaqgitDyrG1ReAyY2GtroWpeR6Yj7jQepuR4Y3BP4UfRalJt0ME9X9Z0fslUKih0Ojhqa2GuroYQosNRI31TS0pEYgYDJuoenmk19ua5L+2pM1hRa7LDGp8GuRCcUBtGkuNUuO/y/pDL236v2VxUCYVchjStGsN7JnVj686NUucxYFLT8esUcM0jEc0pPJxM641REIUfuUcw77ABqtiOj/EM7hV8WdP5t7vPxTiSYoMlRoOFZhsSPCa7+WNocl2NUtutUGdmdEcTiaBMb5lUaa/u+CqT0eqAMJkgEwIau5U592FEJpOhvTEEp1PgYEUj7E4BnTYmsoJ7z6uhtYGl5Tg4mbZN0XVdh6JDZ8phetbEl0fOJCKKXPKkJFiUMYDTiYaq9keahBDQG10jTLF2S/iuEE1RR5kRXHCvN9vhNJmgdlihiFFBHh8dVYMuBE1mO+xO1zy15Lj2BxvCjdc8pgCuMAGslNMeBvcUfjzTcJwBThpiWg51s4TkBOl2Y0X7QZPF7oTN6ErLiVcrIYuJrA9eilzK9AwcS+qBjb3HYVW5HKfq2q6Y43AKGKyu4D7eZoYiNZWTFCNInUcFndQIC+4VOo+c+wDTchx1LRO/Pb8cENNyKBx1ZpVazy8BDO6pGySmJgIoA4AOR+6bzDY4m4N7bXzkrnhKkUeZkQ67TIE6TSJiDGY0mGzIbWNfvcUO4XBCNAf3nik9FB7qjVZsP1aLWr0F/TMSML5fS1Bb3dRSQlKnjazgvjNpOZ5fAhRc9dsLoyAKP55pNZ1Jy2HOPXWDhLSWCVxNNe0vvKKvawCaFyzTJrAWM3UfpU6HeLurBKbTaERjG4sfAc1fQptL2sbbTVCm9+yWNlLgZDIZDp9pAgBoNSavbZWNLaVOMxM13dquc6XUeZYXDnTkvmU/jtx7Y1oOhR/P4NwRbFqOLOgVaok6IymjZaSpoa6hnT1dl4+TzU3Q2K1ISNae76YRSWRKJZK0rkDPaTCg0dxecG+H02gAAMTZzJwbEoYSNUrExbg+4yoazNJK4UBLcB+jlCMlLrLmnsnj4iCLc83vCKSqE9B65J4Tvz0xuKfwI1MAaM7zDHTk3r2fXIl2ywkQdZGU7JaKNw0eC1T5k21pwLUnvsbNR7diZHpkjahR5EtKcS2a5jQa0dDOyqZNZjucBlf6WJzdwrScMCSTyZCd7KogZ7E5UWtw9afRakeT2fU5mJ6gjsi5Eu7R+0CDe++Re34R9cTgnsKPTNYyqTaQCbVCeAf3RN0gvmc2YpqvLDU0Gtrd17NKCQMm6m6xGTpo7FbA6UR9XVOb+3nODYm3maBMZ8AUjnKSWwYIKupdo/VnGiI3JcfNHaA7GxrgtHa84JrnyD0XW/PG4J7CkztId9oB4Wx/X6cDQPOlSebbUzdRpKYiXri+VOr13pfHW/NcxZbBPXU3ZXo6tDZX0N5U3wi7w/976rCcJFyGWgyrOY4Eq5Gv1TCVndSy9sups65+PVHbMsCQkxzA2jBhSJnRcjU0kJW/7e5qOUol5EmRU9O/OzC4p/DkWQ6zo4o5Tta4p+4nk8uRoHVVvrE16dHUTi6zvapSuu35AUbUHVQZGYi3uUZ2HXq9lL7RWmaiBgMbyjGq5ijUTjtTHcJUZqIGGpUr776kxgCbw4kRPZMxvp8OOcmxyEuNzLUJvIP7qg73d9S4quooWbLVB4c5KTwpPMp4OW0A2inrxTKYFCIj453ofXQ/tDYTNBYT0MYqtf+tksOeezFSzU3on53dza2kC50yKwtaq2uE19lkQL3JhpR4/69Ve01LGUKO3IcnhVyG/hla/FDWAKvdiZIaAwZmJiBNq8a4vpFbNcZrwbUOgnvhdEqLWClYKccHR+4pPHnVuu8g986zoo6CI/fUffIyk5Gnr0KqpQnOyjN+97E5nKgw2FERn4aquBSO3FO3U2VnI7E5Lcehb8LZdibVes4PUXDkPmzlZ7YsoneixgCrvYP01QigCmLk3tHQANhdV6CUrHHvg8E9hSfPkfuOymE6GdxTaKg8RuFt5eV+99Gb7XDoXdV0EuPUkKn4GqXupcrORobxLC6qKsZU/XEM9AgM3ZrMNpyqM6K+th4CgCIpCXKupBy2clNjkZ3kmjh7UV4KVIrIT0vxSsup7iC496io41kjn1wY3FN48gzSO6qY4xn8M+eeupGqh0dwX1Hhd5+GRgNEcwWSxCQuYEXdT5mVjQSbCUPrTqBH+XFo1b7piyU1BqzeVYr/aPNxJLknlEwfC2symQxXDctGbIwCtQZLVOScKzMzpdu2ysp29vQe2fdM5yEXBvcUnjyD9I5G7qMoLUev12P58uXo3bs3tm7d6rXtV7/6FdLT03HgwIEu/71LlixBUlIStmzZ0uWPHc2U2dmoUyfgZEImDpb6r81cX9byIZWsS+yuphFJFNp4yBNdrz3bGf/pY2eNNjiNJsDpRILVCFVWVnc2kTohKU6FX4zvjZ4pkTmBtrVgquXYKj2D+8x29rwwcfYhhSdFEMG9MzpG7o8dO4bHH38c//nPf+BwOHy2nz59GmfPnkVDQ/uroXZGeXk5GhsbUeMxmY46psrJwea8AlgVKuiqbLjMzz61FS0fUknprMVMoaHKyoKlsRG2M2cgnE7I5N5je/VGK5x6Vw38RKvR66oUha/YmOhZkV0eHw9ZXByE0dhhzr3dY2Rfmcl5TK1x5ODHHgUAACAASURBVJ7Ck1wByJpfnoGO3MsUgDxyX9L9+vXDO++8gylTpvjd/sEHH6C8vByXXnppp39HRUUFTpw44XP/888/j/LycsycObPTj30hisnJQYLDVWKwodHgt354XWWddDs1kxMUKTSU2VlwQIY6uQb7i0pR32pSba3eCkeTHgqnA3F2M5RZDO6pe8lkMqiaKzTZg0jLUbFIgY/IjYQo+rkn1TqsrlVo/RGipQ5+hKfkuKW3UX5OpVIh4xzfxFasWOE3uJfL5chmjm3QZEolkrWuSW32hgafgAkA6mrqXfsKAV0uLx9TaKiys3E0uSc29hmPT/aWoqSmZdEjs82BBpMNTr0eKZYmyACospmWQ93PPdfDaTDA0djY5n42z7VDMvm+2hqDewpfUsUc4b1QlSfhANA8WhrBKTmeVOepmsq+ffuwYsWK8/LYFzJdWvPKiDYbasu8LyULIVBb50p10NpMiO3Vq7ubRwTAlUKmM7tS+hyNTahstEjbqptct516PVLNrtcrc+4pFFQ9eki32ypSAAB2d869TMbF1vxgcE/hy6scZht1maNoMu35VFpaihtvvBHG5qot1HV0Hqk2NSfLvLYZrA5Y6l0BVYLNBFVeXre2jcgtJq8XUix6yIUTjoYGVDWZpW3Veldw72hqQorZNVqqzO7h93GIziev8sJl/ssLAy1pOQqdjuWF/WBwH6Dy8nI8+OCDGDp0aMDHrFmzBmPHjkVKSgr69++PJ554AjZbB/nj1KIbg/sffvgBs2bNwp133gmHw4GFCxdCp9MhPT0dDzzwACwW14ef3W7HqlWrkJ+fj61bt+LTTz9Fbm4uCgoK0OhxCfHVV1/FtGnTMHLkSOh0Otx22204deqUz+8tLi7GLbfcgkGDBmHEiBGYPXu23wmzpaWlWLRoEXJzc32q6ADAhg0bMHXqVBQUFKBPnz6YMWMGDh8+LB07d+5cNDW5RuTmzZuHgoICPPzwwwCA6upqPPPMM8jPz8fKlSt9Hnvv3r249dZbMWHCBPTu3RuXXHIJVq1a5dO+ZcuWYdy4cZgzZw5MJhMeeeQRZGdnIzk5GQsXLoRoK7UqwqX3bLkkXHnaO09UKQfGHv0WQ2uOo6/aDrla3d3NIwIAxOTlQiGcSLbo4WhoQJ3BKi18VNXoMXJvaQJkMqhYXpBCwHvk3n9wLxwOaSVl5tv7x+C+AyUlJbjnnnvQt29f/O1vf4PBYOjwGCEE5s2bhzvuuAO///3vUVNTg/Xr1+P111/HtGnTYDKZuqHlUUDpEdzb2wruWy4te30ZCMIDDzyAgoICvPvuu3A4HLj//vvx6quvwmazoaamBsuXL8cvfvELFBUVYeLEiZg9ezaKi4tRUlKCOXPm4PTp09i9ezd27NgBALj77rtRVlaGjz/+GPv27cOaNWuwdu1ajB8/HtUeqz/u3r0bBQUF6N+/Pw4ePIi9e/eiR48eWLt2rVf7Dh06hBdeeAFLly7F6dOnfdr/17/+Fb/+9a/x6quvYteuXdi6dSvWr1+PCRMmoKysDHl5efjkk09w7bXXAgBefvll7Nq1C0899RQqKyvxt7/9DUuXLkVxcbHPY2/cuBGXX3457rnnHnzxxRc4duwYLrvsMsyePRv33HOPtF9JSQlKSkrw7bfforq6Gvfccw/GjBmD//73vxgyZAieeuoprF69ulP9E+4y+vaErPmLS9WZOq9tyqZG9Ck/gotqjmJoBmvcU+iocl1XjXSmBjgaGyAEUF5vghACZfUmAAKi/iySLXoos7Ig4wJWFAJea4e0sTCgvaYWaK4ox3x7/1gKsx1CCGzevBmzZ8/Grl27sGvXroCOe/rpp/HKK69g2bJluOmmmwAAgwYNwqpVqzBx4kTMnz8fb7zxxvlsekB2nzyL70rPdrhfeoIaN4zK8brvg71lUp5mey7KS8HoXinS/y12B976+mRA7btucDKk09ZhxfFqPf53qMr7cT1H9JWdGxV97rnnMHr0aNxxxx3YsWMHfvrTn6Kq+ZLfwoUL8cwzz+C9997Dfffdh+3bt2PSpEnYtm0b3nrrLezfvx+7du3Cpk2bMGHCBHz44YfYvn079u/fLz3+5MmTccUVV2Dt2rVYsWIFFi9eDLPZjJkzZ2LIkCF48sknAbgqBSxZsgT/+te/cPJky99o0KBBeOqpp3Dw4EF8+OGHXm3/4osv8Oijj+L9999H3759AQC9evXCRRddhB07duDTTz/F7Nmz23zumZmZ+Mtf/gK9Xo+///3vXtvq6uowe/ZszJo1C5dffjkAQKFQYNmyZdi6dSteeuklTJ48GTNnzsSkSZNgMBjwwgsvoKKiAv/+978RF+eqvfyHP/wBV199NdatW4dbbrmlU30UzuIH9Eei1YAGtRbV1fVwOAUUcteCMtYTLf0Y05v59hQ6Cm08FDodsg21ONbguspYUmNAdrIGqfEq6BuakHG2Agrh5GuVQsYzLcfeRs69rbwl/VGZxeDeHwb37ZDJZLjrrrsAAD/96U8DCu7Ly8uxaNEiqNVq6Vi3CRMmYMSIESgsLMScOXMwYcKE89LuQFntTjSZ25io6sHfaoYmqyOgY92XfT0FchwAOGSete6tsDuFdKz0uJ4j+p0cuQeAnj17AgDy8/Pxxz/+Ubr/6aefxv/+9z/s2bMHa9aswY9+9CPkNedNz5s3D6mpqZg+fTqmT58OwDUqXlFRgXHjxnk9/tmzZ5GTkyMF7W+//TaOHTuGBQsWeO0nl8sxfvx4r+DeLSkpyee+Z599FjKZDFdffbXX/W+++SY2b94sfbnsiL/HLiwsRHV1tRTYe7Zx/vz5uOuuu7B06VKpfKZG46oaM3z4cCmwB4A+ffoAACo7KG0WqVQ5OUiFFQ0ArLW1qDNYkZ7g+qJp9ahMFNO7d0jaR+QWk5uLrP3fA/omCLsNx2sMmJyfjp9c1BMNzmoUnyly7ceJ3xQiygBy7j3vj2n+7CZvDO4D5C/48eeVV16B2WzGxIkTkZCQ4LP9iiuuwP79+7F8+fKQB/cxSjkSNB2/BPwtkhEbowjo2Bilb+ZXIMcBgEKpBCwyAAJwWKGUy6Rjpcf1HLk/h5x799LdrftZJpPhtttuw549e3DkyBHXr1G4/h6Zfi4H7ty5E1OmTMF7773X7u9zp97k5+f7bFO3kZft/r2etm/fjvT0dMS0uoSen5/v97Hb4u+xP/roIwBAmp9KBO7X7p49e2A0GhEXFwd5G2sMuIN+q7WN1KoIJ5PJkJmZivIaK5LPlMLW2AgkpMPpFDhYdAIKVSy0NhNi+vQNdVPpAhfTKw8xe/ci01gHU0MjGpUqHK8xoF+6FjhdCq3N3Lxf79A2lC5YcrUairQ0OGpq2kzLsXmkp6pyGNz7w+A+QEplYH+qDRs2AACGDRvmd/vIkSMBAOvWrYPJZEJsbGzXNLATRvfyTpkJRus0nUCplQrMnRBEkGNRA3YzYLegb3Y8+rY+1h3cy1Uti151MXeQ7G/V2Nbq6ur81pFv7dixYwBwzv1fV+fK8bbb7QG/RgPlngB89qxv6lZubi4AV+qayWTyGqm/EF3cR4d+37wDGYDk8hNATjrqjFZ8XG6Bvd8E9Gkox8ChQ0LdTLrAxTRfRetfX4a9dXWISUtDQvOVWavH1UKO3FMoxeTmwlRTA3t1NZwGA+Tx3vOVbGUewT1H7v3ihNouVFtbi927dwOAlLrRWq/mN02bzYa9e/d2W9silpRHL3wr5jgdLfXvledv8pd7RDonp+MvNFqtFnv27MGZM2f8bnf3ufsx3cF5ZyUmJsJut7eZMnbw4MFzemwAfqv8uK8UaLVa6HS6Tv+OaBGXnw9Z823zIVeVojP1RjiqXRUddFoNlKmpIWodkYt64EAAQF5TJdLrKyGTARmJzVfWOD+EwoRnCqO1tNRnu9Vj5D6mZ+cGGqMdg/suVFJSIpX769nGt0nPQOjQoUN+96moqMCePXukH3dAuHfvXq/7K9pZ4CFqeE6StbeawOvomnx7T06n7xwBd/77pEmTOjx+1KhRsNvtePTRR322lZaWYv369QCAwYMHA4BUYaezRo8eDQBYtmyZz7bjx4/jk08+6fRju5+v+2qUp5rmMmTXXHNNpx8/mmg8RuVN3+0BABw5eBKiufRtz96c9EWh5w7uZQAuO7UXw3ISpW2W48ddN+RyjoZSSHleObL6mX9mO+2aUCvXaiEPMGX6QsPgvgt5ljn0l28PeOdT+0t3AFyTMkePHi39uIOsSZMmed3/8ssvd2Hrw5RS03Lbbvbe5vl/z/3OQVlZmc99H3zwAbKzs6VKL+4vcHa778TgO++8E4BrMuq9996L2tpaAEBRURFuvvlm6TFuv/12AMCqVauk+vOttc5Rd6cFeX4Bcf++1atX48knn5TWUdi3bx9mzpyJGTNmSPu6R9v9tdvfY997773QaDTYsmWLlEbk9tVXX0Eul+O3v/2tdJ/7d/v7gtTe/dFAM3gw5FotAKBmxx6UnTXieLFrxEltt6LX4H6hbB4RAFcNcXeKg7K4CFMGub50Oi0WWI4eBQCo+/WDnGUwKYQ8rxx5XlECXDXu3SvXqnr2lObLkbeoDe4XLVoEmUzWqZ9Acqb98UyxaCsH2TPAMZvNfveZN28edu/eLf18/vnnAIDPP//c6/558+Z1qp0RRdHOyL3n/7souN+6dSv+85//SP8vLCzEZ599hjfffBMajQZOp1O64vLpp5/6HP+zn/1MCuBffPFFZGRkICkpCUOHDsVNN90k5e/fcMMNmDVrFioqKnDzzTdLX/SKiorw2WefSW0pKSkB4Aq+Dxw4AMA71WbmzJlStZpHH30UaWlp6NGjB0aNGoV7771Xyo0HgP79+wNwLdgFuCZ/u7lLd3o+dt++ffHiiy9CCIFZs2ZJ5UFPnDiBRx55BE8++STGjBkj7b9v3z7pMTxf50ebg4Zjx45F7RoPMqUS6jFjsCNzENakDsO/Nu2D4YQruM8xVCP+4otD3EIiQCaXQz1gAADAVlYGh14PALAUHwGav/RrhnBuCIWWV1pOq3jMVlEhvVZVTMlpU9ROqH3wwQcxd+7cTh2b7VGKKRitK5b447lCbUqK/8ms2dnZXm1wr3w6atQoKQ/6gtHeyL3Nc+S+a1b+vOGGG7B+/XosWbIEZrMZubm5+PzzzzFu3Djs3r0b1113nZQO9fTTT+P999/H/v37pYmxMpkM77zzDsaMGYNXX30VJ0+eRFZWFp566incfffdXr/rrbfewtChQ/HKK68gPz8fU6dOxciRI3HppZciLi4Oer0eRUVFaGhowA033IDS5tzDBx54AHv37sWrr74KwFVWc9SoUXj55ZdRXl6Ovn374u9//7tPGcwFCxZgy5YtWLRoEY4ePYrHHnsMVVVVuOyyy6QAfNmyZfj+++/x8ccfAwBmz56N3NxcPPHEExg+fDj69u2LxMREPPfcc17lN2+++WapAtCuXbvQu3dvvPnmm1i1apVUOai8vBx9+vTBP//5T0ydOrVL+iucJIwdi7PHtsAhV8B6/Lj0odRHGBA3msE9hQd1fj5Mzame5u+/R/z48TA3DxwA3ilmRKEQ4zFnsXVajqW5ah0AqPvyimhboja4T0xM7PZA2DOfvq0RyoaGBum2vxKD1IpCCciVromzdjMgBOC+DCcF+7IuC+4TExOxcuVKv9tGjx6N8jZKc3k1WaHAQw89hIceeqjD/R577DE89thjHT6mv7r3bkqlEgsXLsTChQvbfYzY2Fhs2rTJ5/4jHm+W/kydOrXDYLyt1WcnT56MwsLCdo+NFgmTJ2HEilfwv9zRMDbPpchrrET+2BGQqTpfppWoK8UVjEZ98xdu486druC+qEjarhk6NFRNIwIAyOPioMzKgv3MGViOHYMQQkq/caePAZCuQpGvqE3LCQX3JEmg7QV73DnYQMviPtQBVXO5SKcdcDRf+RCiJbhXqs9bGUyiQMX07o0B40bh8tN70LOpCn0ayjH2zAEkXnllqJtGJInzSKUzNH8JNe7c6bpDLoc6f1AomkXkRdOcwupsbPRatMrqFdz37/Z2RQpGRF0oOzsbffu66rC7c6Vbc4/A6nQ6XMw83MCoPOYv2Iyuf+0WAK6JrV0xau9vQilRsNLuuRs51kZMLtuLyyp+QNrUy6GdPDnUzSKSqLKyoOrlSnsw7dsP04EDsDZ/XsVefBEU2vj2DifqFuohLYOl5qKWtDGz+0qzQiGt20C+GNx3MXees7vefWvuiYvTp09vc0VPasVfcG/Ve2w/9w8j90TSQ4cOBbRYFZE/scOHo9fbbyHphuuRfMvNyH5iCas5UNiJHzfedcNmQ9lvHpTuT5gSfXNhKDJ5Tuw2N38+C4cD1mOukq0xeXmQt7GaOzG473L3338/YmJisG3bNhiNRp/t//vf/wAA8+fP7+6mRa4Yj+DeavD+FwBiOh/cm0wm9O/fHwsWLAAA7Ny5E3l5edi4cWOnH5MubLEjR6LHU08he/FiKC60CfAUEZKbK3oBgM1jkaCEqVNC0RwiH5rBLcG9pcgV3FuKiyEsrip57jUbyD8G9wFyl620WCzt7tezZ088/vjjMJlMePPNN722ffTRRzhy5Ah+/vOf47LLLjtvbY06clXLIlVWgyv3Xhq5l3kH/0GKjY3F0aNHIYSQfsrKyrwqwRARRZPYYUMRV1DgdV/CtB97LR5EFEqqnB5QNC9QZdy7F8JulwoVAPB5/ZI3BvcBMBgMWLduHQCgqqoKX3zxRbv7P/LII7jxxhuxcOFCbN26FUIIbNmyBb/4xS8wZcqUC2Pxqa4kkwEa9yp0AtBXt6xOGxPPybREREHKfmIJYvq5SgmqeuUh6//+L8QtImohk8kQN96VPuZsaIBp/34YduyUtsddckmomhYRorYUZle57rrrsHHjRmmipdPpxMSJE6FWq/HNN99g1KhRPscolUqsXr0ay5cvx913343Kykr07dsXTz75JGbPng2FQtHdTyPyaZIAQ/MKwPoz3vcTEVFQYnr1Qt8P18F6/DhU2dnSyrVE4UI7cSKamtdcadq8BcZduwAAiuRkVsrpAIP7Dnz44YedOk6hUODBBx/Egw8+2PHO1LEYrSs9x9myCBhkciAuNXRtIiKKYDK5HOr+DJIoPGkn/Ei6XffGG9LtuPHjIGNBknbxr0ORQSYDknO974tPdy1wRURERFFFmZ6O+AkTfO5PveOOELQmsjC4j1JCiFA3oetpkoCkXECd6Po3ITvULaJuEJWvZSIi6lDmIwsBjxW+4y8dj7iLLgphiyIDhz2jlLvyS9TV2I5Pc/3QBYGBPRHRhUvdty96/u1ZNHy4HjG9eiH19p+HukkRgcF9lEpLS0NVVRUyMzND3RSiTquqqoJOpwt1M4iIKEQSfvxjJPz4x6FuRkRhWk6U6t+/P/bv38+RT4pYQgjs378f/Tnhj4iIKGAM7qNUSkoK+vTpg82bN6OyspJBPkUMIQQqKyuxefNm9OnTBykpKaFuEhERUcRgWk4U69+/P3Q6HY4ePYrvvvsOAKIvB5+iivtLqE6nQ0FBAQN7IiKiIDG4j3IpKSkYM2ZMqJtBRERERN2AaTlERERERFGCwT0RERERUZRgcE9EREREFCUY3BMRERERRQkG9xT1KioqsGjRIlRUVIS6KRcs9kHosQ9Cj30QeuyD0GMfnH8M7inqVVRU4M9//jPfSEKIfRB67IPQYx+EHvsg9NgH5x+DeyIiIiKiKMHgnoiIiIgoSnARqwjgXrWzsbExxC2JTHq9XvqXf8PQYB+EHvsg9NgHocc+CD32QeDcfx93HBgomQj2COp2p0+fRm5ubqibQURERETd7NSpU+jZs2fA+zO4jwBOpxPl5eVISEiATCYLdXMizt69ezFp0iR8/vnnGDVqVKibc0FiH4Qe+yD02Aehxz4IPfZB4IQQaGpqQo8ePSCXB55Jz7ScCCCXy4P6xkbetFqt9G9iYmKIW3NhYh+EHvsg9NgHocc+CD32QXCSkpKCPoYTaomIiIiIogSDeyIiIiKiKKFYtGjRolA3guh802q1mDx5MhISEkLdlAsW+yD02Aehxz4IPfZB6LEPzi9OqCUiIiIiihJMyyEiIiIiihIM7omIiIiIogSDeyIiIiKiKMHgnoiIiIgoSjC4p4hgt9uxatUqDBo0CFu3bu1w/6NHj+K2225DdnY20tPTMXPmTBw7dqzD47777jtcd911yMjIQFZWFu666y5UVVV1wTOIfMH2AQDYbDYUFhZi4MCB+PLLLwM65siRI5g1axays7ORkZGBWbNmoaSk5BxaHj2C6YMffvgBP/3pT5GZmQm1Wo2+fftiwYIFqKio6PD38DxoWzB9UFJSgp///OfIyMiAWq3GwIEDsWjRIpjN5g5/z2effYYpU6ZAp9MhNzcXDz30EPR6fRc9i8jWmfciT08++SRkMhlWrlzZ7n48D9rWmT4YO3YsZDKZz8/o0aPbPIZ90EmCKIyZzWbxj3/8Q/Tq1UsAEADEZ5991u4xmzZtElqtVtx5552ioaFB6PV6MXfuXJGYmCi++eabNo9buXKlUCqV4o9//KMwGo2itrZWXHvttaJHjx7i6NGjXfzMIkdn+sBkMonnn39e5OXlScd88cUXHf6ujRs3ivj4ePGrX/1KNDY2iqamJjFnzhyRlJQkdu7c2UXPKPIE2wcfffSR0Gg00r6eP+np6WLPnj1tHsvzwL9g++DQoUNCp9MJlUoldDqdVx9cddVV7f6uJUuWCIVCIV588UVhtVrFqVOnxJgxY8SQIUNEdXV1Fz+zyNGZ96LWvvvuOxETEyMAiMLCwjb343ngX2f74JNPPvH7fgRAvP/++36PYR90HoN7CmsfffSR2LZtm3j88ccDeiM5evSo0Gq1YtSoUcLhcEj322w2MXDgQJGeni5qamp8jvvyyy+FUqkU1113ndf9jY2NIjk5WQwaNEiYzeYue16RJNg+EEKIDRs2iG3btomFCxcGHNwXFxeL+Ph4MXr0aJ++69evn8jMzBR1dXVd8ZQiTjB9UFNTI1JSUsSECRPE2rVrxeHDh8XWrVvFNddcIx2bm5srDAaDz7E8D9oWTB9YrVYxfPhw8fzzz0t/r+LiYnHppZd2GNC8++67AoBYsGCB1/0lJSVCoVCIKVOmdOnziiSdeS/yZDKZxNChQ4VMJms3uOd50LbO9sGkSZPEypUrxcGDB31+nE6nz/7sg3PD4J4iQk1NTUBvJNdff70AIF555RWfbc8++6wAIO68806v+51Opxg5cqQAID755BOf4+6//34BQCxevPicn0ckC7QPPJ05cybg4P7qq68WAMQbb7zhs+3pp58WAMS8efM60/SoEUgf/PWvfxW33367zwem0+kUt912m3T8a6+95rOd50HHAumDF154Qaxfv97n/srKSpGcnCwAiF//+tc+241Go8jMzBQARHFxsc929/vbW2+9dc7PI5J15r1ICCEeeOABceONN0qjzv6Ce54HgQmmD7788ksxdOhQv0G8P+yDc8fgniKCzWbr8I3k+PHj0j7Hjx/32X7gwAEBQKjVaq/R+88++0wAECqVSlgsFp/jNmzYIACIrKwsYbPZuuw5RZpA+qA1k8kUUHBfXFws7VdaWuqzfd++fQKA0Gg04uzZs519ChEvkD646aabhF6v97utsrJSKJVKAUDce++9Xtt4HgQmkD5ob0TxyiuvFADEn/70J59thYWFAoDIy8vze+wLL7wgAIjRo0d3pulRozPvRVu2bBHZ2dmiurq63eCe50FggumDq666SixatMjrimx72AfnjhNqKSIolcoO99m4cSMAID4+Hn369PHZnp+fD41GA4vFgrVr10r3b9iwAQAwYMAAxMTE+Bw3cuRIAMCZM2fw+eefd6r90SCQPujsMe6+S0xMRG5urs/2IUOGQKVSwWw244MPPgi6HdEikL/nX//6V8THx/vdlpGRgaFDhwIANBqN1zaeB4EJpA/UanWb2zQaDRQKBW677Tafbe4+GDZsmN9j3X2we/fugAoERKtg34vq6+sxZ84cvPbaa0hLS2t3X54HgQm0D7777jt89NFHWLRoEZKSkjBr1qwO/27sg3PH4J6ixqZNmwDAb3AIAAqFAjk5OQCAHTt2+ByXl5fn97gePXpApVL5HEddp6M+UCqV6NGjBwD2QUf69+/f7nZ34Nk6gOR5cP7Z7XZ88803WLRoEQYOHOi1zel0YvPmzQDa7oNevXpJt9kHgZs/fz6uvfZaXH311R3uy/Ogaz3xxBPSbb1ej3fffReTJ0/GTTfdhPr6er/HsA/OHYN7ihrHjx8HAPTs2bPNfXQ6HQDg0KFDAR8nk8mQkpLicxx1nc72HQVHCIFjx45BrVbjxhtv9NrG8+D8W7JkCW655RY8/vjjPtvq6uqkYKetPnCfAwD7IFD/+c9/sHv3bixdujSg/XkedB0hBH7yk59gxYoVuO+++zB48GBp2/vvv49LL70UNTU1PsexD85d8NfZicJUdXU1ACAhIaHNfdyjlmfPngUAmM1mGAyGoI+jrtWZvqPgffvtt6itrcUDDzwgfTgCPA/OtzNnzuAPf/gDXnvtNQwfPhybN2/Gj3/8Y6993OcA0HYfeKb7sA86Vl5ejgULFmD9+vWIi4vrcH+eB11LJpPhZz/7mdd977//Pn7zm9+gtLQUBw8exM9//nN8/PHH0nb2QdfgyD1Fjbq6OgBo903c6XQCgLSITG1trbQtmOOo6wghpBFL9sH59dxzzyE7Oxt/+tOfvO7neXD+PPfcc5g+fTpee+01AMD333+Pq666CmvWrPHaz/3+BbTdB+6/P8A+CMQvf/lL3HfffSgoKAhof54H59+MGTOwd+9eDB8+HIArBeezzz6TtrMPugaDe4oa/ibetGaz2QBAGrUM5Bh/x1HXkclkfRUfSwAAClBJREFUAU3OYh+cm6+//hqrV6/GypUrkZyc7LWN58H588ADD2D//v0oLi6WJtHa7Xbcfffd0gglENz7F8A+6Mjzzz+PpqYmPPLIIwEfw/Oge6SkpGDTpk1ITU0FAPz3v/+VtrEPugaDe4oa7nxUk8nU5j4NDQ0AIFVMSE5OhkKh6PC4xsZGr+Ooa3Wm7yhwBoMBc+fOxf/93/9h+vTpPtt5Hpx/AwYMwD//+U/pqklNTQ0++ugjabtnPn1bfeA+BwD2QXsOHz6MxYsX46233pJe14HgedB9srOz8fvf/x4AvCo/sQ+6BoN7ihruyTqVlZVt7uO+5OculalSqdCvX792jzMYDNKlP38lNuncdabvKHC/+tWvMH78eDz66KN+t/M86D6PPfYYsrKyALRMHASA3r17IzY2FkDbfeCZssA+aNszzzyD2tpajBgxAlqt1uentLQUAHD33XdDq9VK5WF5HnSvGTNmAIBU+cZ9m31w7hjcU9T40Y9+BAAoKSnxu91gMEgz86dNmxbwcSdPnpRuex5HXaejPmhsbJTy8tkHwVm0aBFMJhNefvnldvfjedA9VCoVrrrqKgCQgnkAkMvlGD9+PICO+0Aul2PKlCnnuaWRy2azweFwwGAw+P0RQgAALBaLdJ8bz4Pu4y7tmp+f73U/++DcMbinqHHTTTcBcFVIOHPmjM/277//HoArp8/zg9F93L59++BwOHyO279/PwBXbV334hnUtdx9UFpa6rc0mrsPNBoNLr/88m5tWyR75ZVX8MUXX+Cdd97pMD2B50H3cY/cX3LJJV73u/tg9+7dfo9z98HYsWOlfGXytXLlSggh2vxxB5WFhYUQQuDEiRPSsTwPuo+70s2tt97qdT/74NwxuKeoMWTIEGlEzDOX1W3Lli0AgDvuuMOrxNZVV12FoUOHwmAwYNu2bW0ed++9956PZhOAESNGSCMw7fXd7NmzAyppR64A54033sDatWv9rphqt9uxevVq6f88D7pPcXExRo0ahbFjx3rdP3v2bKSnp+Pw4cN+Ry3dfTB//vxuaeeFiOdB99m4cSN+8pOfYPTo0V73sw+6gCCKACaTSQAQAMTHH3/c5n6HDx8WsbGxYty4cV73GwwG0adPH6HT6cSZM2d8jtu6dauQyWRi5syZXvdXVlaKhIQEMWDAAGE0GrvmyUSoQPvAU1NTk3TM5s2b2923qKhIaDQacdlll3ndr9frRV5enkhPTxdVVVWdbn80CLQPVq1aJQYNGiSKi4tFdXW19FNVVSVKS0vFJ598IqZOnSrefvttr+N4HnQskD5oaGgQR48e9btt7969IjExUezevdvv9jfffFMAEA8//LDX/T/88IOQy+ViwoQJwul0ntuTiHCdeS/y1KtXLwFAFBYW+t3O86BjgfRBVVWVWL16taitrfXZdubMGXH55ZeLmpoav8eyD84Ng3sKe06nUxQWFkpvJPPnzxdms7nN/d99912hVCrF73//e2E2m0V5ebm45pprREpKivjqq6/aPO6ZZ54RMplMLF++XNjtdlFcXCwuueQSkZeXJw4fPnw+nlrECLYP3Me8+uqr0jH3339/h8e8/fbbQqFQiEceeURYLBZx+vRpceWVV4rU1FTxzTffdOVTijiB9sE//vEPIZPJpP3a+tFqtUKv1/scz/OgbYH2wfDhwwUAMXbsWLFhwwZhtVqFw+EQ69atExdddJH4+uuv2/099913n1CpVOLf//63cDqdYteuXWLAgAFixIgRorKy8nw9vYjQmfei1joK7oXgedCeQPtg7ty5AoDIzMwUhYWFwmg0CrvdLjZu3CjuuuuuDl/L7IPOY3BPYe29994TKpXKJzCRy+Xit7/9bZvHffnll2Lq1KkiNTVV9OrVS8yfP19UVFR0+PvWrVsnxo0bJ5KTk0X//v3Fo48+Kurr67vyKUWczvTBP//5zzaPaT0i2dq2bdvElClTRGpqqujdu7dYsGDBBR/QBNoH//3vfzsM6t0/t99+e5u/j+eBr2DOg1WrVon8/HwRExMjVCqVyMnJEddcc41Yvny5MBgMAf2+wsJCMXLkSJGUlCSGDBkili5dKkwm0/l4ahGjs58HrQUS3AvB88CfYPrg5MmTYsaMGSItLU3ExMSI3r17i1mzZokPP/ww4N/HPugcmRDN08aJiIiIiCiicUItEREREVGUYHBPRERERBQlGNwTEREREUUJBvdERERERFGCwT0RERERUZRgcE9EREREFCUY3BMRERERRQkG90REREREUYLBPRERERFRlGBwT0REREQUJRjcExERERFFCQb3RERERERRgsE9EREREVGUYHBPRERhr7i4GIMGDUJeXh4qKipC3RwiorClDHUDiIiIOvKXv/wFhw8fBgCUlpYiOzs7xC0iIgpPMiGECHUjiIiI2tO7d2+cPHkSsbGxqK+vR0xMTKibREQUlpiWQ0REYe3UqVM4efIkAGD8+PEM7ImI2sHgnoiIwtoXX3wh3Z40aVIIW0JEFP4Y3BMRUVjzDO4nTpwYwpYQEYU/5twTEVFYGzZsGA4cOAC1Wo36+npoNJpQN4mIKGxx5J6IiMJWXV0dioqKAABjxoxhYE9E1AGO3BMRUViwWq3IyMhAQ0NDQPurVCpUVFRAp9Od55YREUUOjtwTEVFYiImJQUVFBZqamqSfGTNmSNu//vprr22NjY0M7ImIWuHIPRERha2ePXuirKwMSUlJqKurg1zOMSkiovbwXZKIiMJSSUkJysrKAACXXXYZA3siogDwnZKIiMKSZwnMCRMmhLAlRESRg8E9ERGFJQb3RETBY849ERGFpUGDBuHw4cPQaDSor6+HWq0OdZOIiMIeR+6JiCjsVFdX4/DhwwCASy65hIE9EVGAGNwTEVHYYUoOEVHnMLgnIqKww+CeiKhzGNwTEVHYcQf3CoUC48ePD3FriIgiB4N7IiIKK3q9Hnv37gUAjBw5EomJiT77LFu2DMuWLevuphERhT0G90REFFa2b98Oh8MBwH9Kjl6vx+LFi3HJJZd0d9OIiMIeg3siIgorRUVF0m1/KTkvvPACBg0axFx8IiI/lKFuABERkSez2SzdHjJkiNe2I0eO4IknnsDHH3/c3c0iIooIHLknIqKwcvHFF0u3VSqVdFuv12PWrFn47W9/i0svvTQUTSMiCnsM7omIKKxMmzYNl19+OQDg7bffRlVVFTZs2IBx48Zh8ODBePzxx0PcQiKi8CUTQohQN4KIiMiTwWDAI488gvfeew+NjY0YNmwYFixYgDvuuCPUTSMiCmsM7omIiIiIogTTcoiIiIiIogSDeyIiIiKiKMHgnoiIiIgoSjC4JyIiIiKKEgzuiYiIiIiiBIN7IiIiIqIoweCeiIiIiChKMLgnIiIiIooSDO6JiIiIiKIEg3siIiIioijB4J6IiIiIKEowuCciIiIiihIM7omIiIiIogSDeyIiIiKiKMHgnoiIiIgoSjC4JyIiIiKKEgzuiYiIiIiiBIN7IiIiIqIo8f8btv1NOIBkWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 800x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "figure(figsize=(8,4))\n",
    "past = 100\n",
    "plot(timevec[N_train-past:N_train+1], s[N_train-past:N_train+1], color = \"C1\", label = \"timeseries\")\n",
    "plot(timevec[N_train:N_train+p], s[N_train:N_train+p], color = \"C3\", label = \"real future\")\n",
    "plot(timevec[N_train:N_train+p], s_pred, color = \"C0\", linestyle = \"dashed\", alpha = 0.5, label = \"prediction\")\n",
    "legend(); xlabel(\"\\$t\\$\"); ylabel(\"\\$y\\$\")\n",
    "println(\"Prediction of $(p) points from $(N_train) points. i.c.: $(get_state(ross))\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course the prediction depends strongly on:\n",
    "\n",
    "* Choosing proper `Reconstruction` parameters\n",
    "* The initial condition\n",
    "\n",
    "How did I know that the value of `τ=17` was good?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "17"
      ],
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate_delay(s, \"first_zero\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `localmodel_tsp` also accepts some keyword arguments which I did not discuss. These are:\n",
    "\n",
    "  * `method = AverageLocalModel(2)` : Subtype of [`AbstractLocalModel`](@ref).\n",
    "  * `ntype = FixedMassNeighborhood(2)` : Subtype of [`AbstractNeighborhood`](@ref).\n",
    "  * `stepsize = 1` : Prediction step size.\n",
    "  \n",
    "We already know what does the `ntype` keyword does: it chooses a neighborhood type.\n",
    "\n",
    "The `method` keyword chooses the method of the local prediction. There are two methods, the `AverageLocalModel`, which we already used by default, as well as the `LinearLocalModel`. Their docstrings are at the end of this tutorial.\n",
    "\n",
    "Without explanations, their call signatures are:\n",
    "```julia\n",
    "AverageLocalModel(n::Int)\n",
    "LinearLocalModel(n::Int, μ::Real)\n",
    "LinearLocalModel(n::Int, s_min::Real, s_max::Real)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  686.934 μs (6029 allocations: 455.09 KiB)\n",
      "Time for predicting 500 points from 1000 points.\n"
     ]
    }
   ],
   "source": [
    "using BenchmarkTools\n",
    "@btime localmodel_tsp($s_train, $D, $τ, $p)\n",
    "println(\"Time for predicting $(p) points from $(N_train) points.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's bundle all the production-prediction-plotting process into one function and play around!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 800x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N_train = 3000, p = 500, method = LinearLocalModel, u0 = [0.464251, 0.919737, 0.68184]\n"
     ]
    }
   ],
   "source": [
    "function predict_roessler(N_train, p, method, u0 = rand(3); ntype = FixedMassNeighborhood(5))\n",
    "    \n",
    "    ds = Systems.roessler(u0)\n",
    "    dt = 0.1\n",
    "    tr = trajectory(ds, (N_train+p)÷dt; dt = dt)\n",
    "    \n",
    "    s = tr[:, 2] # actually, any of the 3 variables of the Roessler work well\n",
    "    \n",
    "    s_train = s[1:N_train]\n",
    "    s_test = s[N_train+1:end]\n",
    "\n",
    "    # parameters to predict:\n",
    "    τ = 17\n",
    "    D = 3\n",
    "\n",
    "    s_pred = localmodel_tsp(s_train, D, τ, p; method = method, ntype = ntype)\n",
    "    \n",
    "    figure(figsize=(8,4))\n",
    "    past = 100\n",
    "    plot(timevec[N_train-past:N_train+1], s[N_train-past:N_train+1], color = \"C1\", label = \"timeseries\")\n",
    "    plot(timevec[N_train:N_train+p], s[N_train:N_train+p], color = \"C3\", label = \"real future\")\n",
    "    plot(timevec[N_train:N_train+p], s_pred, color = \"C0\", linestyle = \"dashed\", alpha = 0.5, label = \"prediction\")\n",
    "    legend(); xlabel(\"\\$t\\$\"); ylabel(\"\\$y\\$\")   \n",
    "    mprint = Base.datatype_name(typeof(method))\n",
    "    println(\"N_train = $(N_train), p = $(p), method = $(mprint), u0 = $(u0)\")\n",
    "    return\n",
    "end\n",
    "\n",
    "predict_roessler(3000, 500, LinearLocalModel(2, 5.0))\n",
    "# Linear Local model is slower than Average local model, and in general not that\n",
    "# much more powerful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Variate Prediction\n",
    "\n",
    "On purpose I was always referring to `s` as \"timeseries\". There is no reason for `s` to be scalar though, this prediction method works just as well when predicting multiple timeseries. And the call signature does not change at all!\n",
    "\n",
    "The following example demonstrates the prediction of the Lorenz96 model\n",
    "\n",
    "$$\n",
    "\\frac{dx_i}{dt} = (x_{i+1}-x_{i-2})x_{i-1} - x_i + F \n",
    "$$\n",
    "\n",
    "a system that displays high-dimensional chaos and is thus very difficult to predict!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delay time estimation: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 1200x400 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction p=200 of Lorenz96 Model (5 Nodes) from 40000 points\n",
      "i.c.: [0.401473, 0.474717, 0.442255, 0.374019, 0.149569]\n"
     ]
    }
   ],
   "source": [
    "using DynamicalSystems, PyPlot\n",
    "\n",
    "#Generate timeseries set\n",
    "ds = Systems.lorenz96(5; F=8.)\n",
    "ic = get_state(ds)\n",
    "Δt = 0.05\n",
    "s = trajectory(ds, 2100; dt=Δt)[:,1:2]\n",
    "\n",
    "#Set Training and Test Set\n",
    "N_train = 40000\n",
    "p = 200\n",
    "s_train = s[1:N_train,1:2]\n",
    "s_test  = s[N_train:N_train+p,1:2]\n",
    "\n",
    "#Embedding Parameters\n",
    "D = 5; # total dimension of reconstruction is D*2 ! ! !\n",
    "x = s[:, 1]\n",
    "τ = estimate_delay(x, \"first_zero\")\n",
    "println(\"Delay time estimation: $(τ)\")\n",
    "\n",
    "#Prediction\n",
    "method = LinearLocalModel(2, 2.5)\n",
    "method = AverageLocalModel(2)\n",
    "ntype = FixedMassNeighborhood(5)\n",
    "s_pred  = localmodel_tsp(s_train, D, τ, p; method = method, ntype = ntype)\n",
    "\n",
    "\n",
    "figure(figsize=(12,4))\n",
    "ax = subplot(121)\n",
    "plot((N_train:N_train+p)*Δt, s_test[:,1], label=\"signal\")\n",
    "plot((N_train:N_train+p)*Δt, s_pred[:,1], label=\"prediction\")\n",
    "ylabel(\"\\$x_1(n)\\$\")\n",
    "xlabel(\"\\$t\\$\")\n",
    "legend()\n",
    "ax = subplot(122)\n",
    "plot((N_train:N_train+p)*Δt, s_test[:,2], label=\"signal\")\n",
    "plot((N_train:N_train+p)*Δt, s_pred[:,2], label=\"prediction\")\n",
    "ylabel(\"\\$x_2(n)\\$\")\n",
    "xlabel(\"\\$t\\$\")\n",
    "legend()\n",
    "tight_layout();\n",
    "println(\"Prediction p=$p of Lorenz96 Model (5 Nodes) from $N_train points\")\n",
    "println(\"i.c.: $ic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatio Temporal Timeseries prediction\n",
    "\n",
    "Spatio-temporal systems are systems that depend on both space and time, i.e. *fields* (like Partial Differential Equations). These systems can also be predicted using these local model methods!\n",
    "\n",
    "In the following sections we will see 3 examples, but there won't be any code shown for the last example (because its humongus).\n",
    "\n",
    "See `TimeseriesPrediction.jl/examples` repository for more examples!\n",
    "\n",
    "## Barkley Model\n",
    "The Barkley model consists of 2 coupled fields each having 2 spatial dimensions, and is considered one of the simplest spatio-temporal systems\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{\\partial u }{\\partial t} =& \\frac{1}{\\epsilon} u (1-u)\\left(u-\\frac{v+b}{a}\\right) + \\nabla^2 u\\nonumber \\\\\n",
    "\\frac{\\partial v }{\\partial t} =& u - v\n",
    "\\end{align}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barkley (generic function with 1 method)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This Algorithm of evolving the Barkley model is taken from\n",
    "# http://www.scholarpedia.org/article/Barkley_model\n",
    "function barkley(T, Nx, Ny)\n",
    "    a = 0.75; b = 0.02; ε = 0.02\n",
    "\n",
    "    u = zeros(Nx, Ny); v = zeros(Nx, Ny)\n",
    "    U = Vector{Array{Float64,2}}()\n",
    "    V = Vector{Array{Float64,2}}()\n",
    "\n",
    "    #Initial state that creates spirals\n",
    "    u[40:end,34] = 0.1; u[40:end,35] = 0.5\n",
    "    u[40:end,36] = 5; v[40:end,34] = 1\n",
    "    u[1:10,14] = 5; u[1:10,15] = 0.5\n",
    "    u[1:10,16] = 0.1; v[1:10,17] = 1\n",
    "    u[27:36,20] = 5; u[27:36,19] = 0.5\n",
    "    u[27:36,18] = 0.1; v[27:36,17] = 1\n",
    "\n",
    "    h = 0.75; Δt = 0.1; δ = 0.001\n",
    "    Σ = zeros(Nx, Ny, 2)\n",
    "    r = 1; s = 2\n",
    "    \n",
    "    function F(u, uth)\n",
    "        if u < uth\n",
    "            u/(1-(Δt/ε)*(1-u)*(u-uth))\n",
    "        else\n",
    "            (u + (Δt/ε)*u*(u-uth))/(1+(Δt/ε)*u*(u-uth))\n",
    "        end\n",
    "    end\n",
    "\n",
    "    for m=1:T\n",
    "        for i=1:Nx, j=1:Ny\n",
    "            if u[i,j] < δ\n",
    "                u[i,j] = Δt/h^2 * Σ[i,j,r]\n",
    "                v[i,j] = (1 - Δt)* v[i,j]\n",
    "            else\n",
    "                uth = (v[i,j] + b)/a\n",
    "                v[i,j] = v[i,j] + Δt*(u[i,j] - v[i,j])\n",
    "                u[i,j] = F(u[i,j], uth) + Δt/h^2 *Σ[i,j,r]\n",
    "                Σ[i,j,s] -= 4u[i,j]\n",
    "                i > 1  && (Σ[i-1,j,s] += u[i,j])\n",
    "                i < Nx && (Σ[i+1,j,s] += u[i,j])\n",
    "                j > 1  && (Σ[i,j-1,s] += u[i,j])\n",
    "                j < Ny && (Σ[i,j+1,s] += u[i,j])\n",
    "            end\n",
    "            Σ[i,j,r] = 0\n",
    "        end\n",
    "        r,s = s,r\n",
    "        #V[:,:,m] .= v\n",
    "        #U[:,:,m] .= u\n",
    "        push!(U,copy(u))\n",
    "        push!(V,copy(v))\n",
    "    end\n",
    "    return U,V\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstructing\n",
      "Creating Tree\n",
      "Working on Frame 1/20\n",
      "Working on Frame 2/20\n",
      "Working on Frame 3/20\n",
      "Working on Frame 4/20\n",
      "Working on Frame 5/20\n",
      "Working on Frame 6/20\n",
      "Working on Frame 7/20\n",
      "Working on Frame 8/20\n",
      "Working on Frame 9/20\n",
      "Working on Frame 10/20\n",
      "Working on Frame 11/20\n",
      "Working on Frame 12/20\n",
      "Working on Frame 13/20\n",
      "Working on Frame 14/20\n",
      "Working on Frame 15/20\n",
      "Working on Frame 16/20\n",
      "Working on Frame 17/20\n",
      "Working on Frame 18/20\n",
      "Working on Frame 19/20\n",
      "Working on Frame 20/20\n",
      "Maximum error: 0.018341198402581194\n"
     ]
    }
   ],
   "source": [
    "Nx = 50\n",
    "Ny = 50\n",
    "Tskip = 100\n",
    "Ttrain = 500\n",
    "p = 20\n",
    "T = Tskip + Ttrain + p\n",
    "\n",
    "U,V = barkley(T, Nx, Ny);\n",
    "\n",
    "Vtrain = V[Tskip + 1:Tskip + Ttrain]\n",
    "Vtest  = V[Tskip + Ttrain :  T]\n",
    "\n",
    "D = 2\n",
    "τ = 1\n",
    "B = 2\n",
    "k = 1\n",
    "c = 200\n",
    "\n",
    "Vpred = localmodel_stts(Vtrain, D, τ, p, B, k; boundary=c)\n",
    "err = [abs.(Vtest[i]-Vpred[i]) for i=1:p+1];\n",
    "println(\"Maximum error: $(maximum(maximum.(err)))\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the real evolution, prediction, and error side by side (plotting takes a *lot* of time) with `Tskip = 200, Ttrain = 1000, p = 200` produces:\n",
    "\n",
    "![Barkley prediction](barkley_stts_prediction.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kuramoto-Sivashinsky\n",
    "\n",
    "This system consists of only a single field and a single spatial dimension\n",
    "\n",
    "$$\n",
    "y_t =  −y y_x − y_{xx} − y_{xxxx} \n",
    "$$\n",
    "where the subscripts denote partial derivatives.\n",
    "\n",
    "We will not show code for this example, but only the results. The code is located at `TimeseriesPrediction.jl/examples/KSprediction.jl` and is quite large for a Jupyter notebook.\n",
    "\n",
    "### Prediction\n",
    "\n",
    "A typical prediction with parameters `D = 2, τ = 1, Β = 5, k = 1` and system parameters `L = 150` (see file for more) looks like this:\n",
    "\n",
    "![Prediction of KS system](KS150_tr40000_D2_tau1_B5_k1.png)\n",
    "\n",
    "The vertical axis, which is *time*, is measured within units of the maximal Lyapunov exponent of the system Λ, which is around 0.1.\n",
    "\n",
    "### Mean Squared Error of prediction\n",
    "\n",
    "We now present a measure of the error of the prediction, by averaging the error values across all timesteps and across all spatial values, and then normalizing properly\n",
    "\n",
    "![Error of prediction](KS_NRMSE_L6_Q64_D1_tau1_B5_k1_nn4_nw3PndWDWSt.png)\n",
    "\n",
    "The above curves are also *averaged* over 10 different initial conditions and subsequent predictions!\n",
    "\n",
    "The parameters used for the prediction \n",
    "\n",
    "You can compare it with e.g. figure 5 of this [Physical Review Letters article](https://doi.org/10.1103/PhysRevLett.120.024102)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Docstrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1ml\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mm\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m\u001b[1m_\u001b[22m\u001b[1mt\u001b[22m\u001b[1ms\u001b[22m\u001b[1mp\u001b[22m \u001b[1ml\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mm\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m\u001b[1m_\u001b[22ms\u001b[1mt\u001b[22mt\u001b[1ms\u001b[22m Average\u001b[1mL\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mM\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m Abstract\u001b[1mL\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mM\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "localmodel_tsp(s, D::Int, τ, p::Int; method, ntype, stepsize)\n",
       "localmodel_tsp(s, p::Int; method, ntype, stepsize)\n",
       "```\n",
       "\n",
       "Perform a timeseries prediction for `p` points, using local weighted modeling [1]. The function always returns an object of the same type as `s`, which can be either a timeseries (vector) or an `AbstractDataset` (trajectory), and the returned data always contains the final point of `s` as starting point. This means that the returned data has length of `p + 1`.\n",
       "\n",
       "If given `(s, D, τ)`, then a [`Reconstruction`](@ref) is performed on `s` with dimension `D` and delay `τ`. If given only `s` then no [`Reconstruction`](@ref) is done. Keep in mind that the intented behavior of the algorithm is to work with a reconstruction, and not \"raw\" data.\n",
       "\n",
       "## Keyword Arguments\n",
       "\n",
       "  * `method = AverageLocalModel(2)` : Subtype of [`AbstractLocalModel`](@ref).\n",
       "  * `ntype = FixedMassNeighborhood(2)` : Subtype of [`AbstractNeighborhood`](@ref).\n",
       "  * `stepsize = 1` : Prediction step size.\n",
       "\n",
       "## Description\n",
       "\n",
       "Given a query point, the function finds its neighbors using neighborhood `ntype`. Then, the neighbors `xnn` and their images `ynn` are used to make a prediction for the future of the query point, using the provided `method`. The images `ynn` are the points `xnn` shifted by `stepsize` into the future.\n",
       "\n",
       "The algorithm is applied iteratively until a prediction of length `p` has been created, starting with the query point to be the last point of the timeseries.\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : D. Engster & U. Parlitz, *Handbook of Time Series Analysis* Ch. 1, VCH-Wiley (2006)\n"
      ],
      "text/plain": [
       "```\n",
       "localmodel_tsp(s, D::Int, τ, p::Int; method, ntype, stepsize)\n",
       "localmodel_tsp(s, p::Int; method, ntype, stepsize)\n",
       "```\n",
       "\n",
       "Perform a timeseries prediction for `p` points, using local weighted modeling [1]. The function always returns an object of the same type as `s`, which can be either a timeseries (vector) or an `AbstractDataset` (trajectory), and the returned data always contains the final point of `s` as starting point. This means that the returned data has length of `p + 1`.\n",
       "\n",
       "If given `(s, D, τ)`, then a [`Reconstruction`](@ref) is performed on `s` with dimension `D` and delay `τ`. If given only `s` then no [`Reconstruction`](@ref) is done. Keep in mind that the intented behavior of the algorithm is to work with a reconstruction, and not \"raw\" data.\n",
       "\n",
       "## Keyword Arguments\n",
       "\n",
       "  * `method = AverageLocalModel(2)` : Subtype of [`AbstractLocalModel`](@ref).\n",
       "  * `ntype = FixedMassNeighborhood(2)` : Subtype of [`AbstractNeighborhood`](@ref).\n",
       "  * `stepsize = 1` : Prediction step size.\n",
       "\n",
       "## Description\n",
       "\n",
       "Given a query point, the function finds its neighbors using neighborhood `ntype`. Then, the neighbors `xnn` and their images `ynn` are used to make a prediction for the future of the query point, using the provided `method`. The images `ynn` are the points `xnn` shifted by `stepsize` into the future.\n",
       "\n",
       "The algorithm is applied iteratively until a prediction of length `p` has been created, starting with the query point to be the last point of the timeseries.\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : D. Engster & U. Parlitz, *Handbook of Time Series Analysis* Ch. 1, VCH-Wiley (2006)\n"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?localmodel_tsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1mA\u001b[22m\u001b[1mb\u001b[22m\u001b[1ms\u001b[22m\u001b[1mt\u001b[22m\u001b[1mr\u001b[22m\u001b[1ma\u001b[22m\u001b[1mc\u001b[22m\u001b[1mt\u001b[22m\u001b[1mL\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mM\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "AbstractLocalModel\n",
       "```\n",
       "\n",
       "Supertype of methods for making a prediction of a query point `q` using local models, following the methods of [1]. Concrete subtypes are `AverageLocalModel` and `LinearLocalModel`.\n",
       "\n",
       "All models weight neighbors with the following weight function\n",
       "\n",
       "$$\n",
       "\\begin{aligned}\n",
       "ω_i = \\left[ 1- \\left(\\frac{d_i}{d_{max}}\\right)^n\\right]^n\n",
       "\\end{aligned}\n",
       "$$\n",
       "\n",
       "with $d_i = ||x_{nn,i} -q||_2$ and degree `n`, to ensure smoothness of interpolation.\n",
       "\n",
       "### Average Local Model\n",
       "\n",
       "```\n",
       "AverageLocalModel(n::Int)\n",
       "```\n",
       "\n",
       "The prediction is simply the weighted average of the images $y_{nn, i}$ of the neighbors $x_{nn, i}$ of the query point `q`:\n",
       "\n",
       "$$\n",
       "\\begin{aligned}\n",
       "y_{pred} = \\frac{\\sum{\\omega_i^2 y_{nn,i}}}{\\sum{\\omega_i^2}}\n",
       "\\end{aligned}\n",
       "$$\n",
       "\n",
       "### Linear Local Model\n",
       "\n",
       "```\n",
       "LinearLocalModel(n::Int, μ::Real)\n",
       "LinearLocalModel(n::Int, s_min::Real, s_max::Real)\n",
       "```\n",
       "\n",
       "The prediction is a weighted linear regression over the neighbors $x_{nn, i}$ of the query and their images $y_{nn,i}$ as shown in [1].\n",
       "\n",
       "Giving either `μ` or `s_min` and `s_max` determines which type of regularization is applied.\n",
       "\n",
       "  * `μ` : Ridge Regression\n",
       "\n",
       "    $$\n",
       "    \\begin{aligned}\n",
       "    f(\\sigma) = \\frac{\\sigma^2}{\\mu^2 + \\sigma^2}\n",
       "    \\end{aligned}\n",
       "    $$\n",
       "  * `s_min`, `s_max` : Soft Threshold\n",
       "\n",
       "    $$\n",
       "    \\begin{aligned}\n",
       "    f(\\sigma) = \\begin{cases} 0, & \\sigma < s_{min}\\\\\n",
       "    \\left(1 - \\left( \\frac{s_{max}-\\sigma}{s_{max}-s_{min}}\\right)^2 \\right)^2,\n",
       "    &s_{min} \\leq \\sigma \\leq s_{max} \\\\\n",
       "    1, & \\sigma > s_{max}\\end{cases}\n",
       "    \\end{aligned}\n",
       "    $$\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : D. Engster & U. Parlitz, *Handbook of Time Series Analysis* Ch. 1, VCH-Wiley (2006)\n"
      ],
      "text/plain": [
       "```\n",
       "AbstractLocalModel\n",
       "```\n",
       "\n",
       "Supertype of methods for making a prediction of a query point `q` using local models, following the methods of [1]. Concrete subtypes are `AverageLocalModel` and `LinearLocalModel`.\n",
       "\n",
       "All models weight neighbors with the following weight function\n",
       "\n",
       "$$\n",
       "\\begin{aligned}\n",
       "ω_i = \\left[ 1- \\left(\\frac{d_i}{d_{max}}\\right)^n\\right]^n\n",
       "\\end{aligned}\n",
       "$$\n",
       "\n",
       "with $d_i = ||x_{nn,i} -q||_2$ and degree `n`, to ensure smoothness of interpolation.\n",
       "\n",
       "### Average Local Model\n",
       "\n",
       "```\n",
       "AverageLocalModel(n::Int)\n",
       "```\n",
       "\n",
       "The prediction is simply the weighted average of the images $y_{nn, i}$ of the neighbors $x_{nn, i}$ of the query point `q`:\n",
       "\n",
       "$$\n",
       "\\begin{aligned}\n",
       "y_{pred} = \\frac{\\sum{\\omega_i^2 y_{nn,i}}}{\\sum{\\omega_i^2}}\n",
       "\\end{aligned}\n",
       "$$\n",
       "\n",
       "### Linear Local Model\n",
       "\n",
       "```\n",
       "LinearLocalModel(n::Int, μ::Real)\n",
       "LinearLocalModel(n::Int, s_min::Real, s_max::Real)\n",
       "```\n",
       "\n",
       "The prediction is a weighted linear regression over the neighbors $x_{nn, i}$ of the query and their images $y_{nn,i}$ as shown in [1].\n",
       "\n",
       "Giving either `μ` or `s_min` and `s_max` determines which type of regularization is applied.\n",
       "\n",
       "  * `μ` : Ridge Regression\n",
       "\n",
       "    $$\n",
       "    \\begin{aligned}\n",
       "    f(\\sigma) = \\frac{\\sigma^2}{\\mu^2 + \\sigma^2}\n",
       "    \\end{aligned}\n",
       "    $$\n",
       "  * `s_min`, `s_max` : Soft Threshold\n",
       "\n",
       "    $$\n",
       "    \\begin{aligned}\n",
       "    f(\\sigma) = \\begin{cases} 0, & \\sigma < s_{min}\\\\\n",
       "    \\left(1 - \\left( \\frac{s_{max}-\\sigma}{s_{max}-s_{min}}\\right)^2 \\right)^2,\n",
       "    &s_{min} \\leq \\sigma \\leq s_{max} \\\\\n",
       "    1, & \\sigma > s_{max}\\end{cases}\n",
       "    \\end{aligned}\n",
       "    $$\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : D. Engster & U. Parlitz, *Handbook of Time Series Analysis* Ch. 1, VCH-Wiley (2006)\n"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?AbstractLocalModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1ml\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mm\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m\u001b[1m_\u001b[22m\u001b[1ms\u001b[22m\u001b[1mt\u001b[22m\u001b[1mt\u001b[22m\u001b[1ms\u001b[22m \u001b[1ml\u001b[22m\u001b[1mo\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mm\u001b[22m\u001b[1mo\u001b[22m\u001b[1md\u001b[22m\u001b[1me\u001b[22m\u001b[1ml\u001b[22m\u001b[1m_\u001b[22mt\u001b[1ms\u001b[22mp\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "localmodel_stts(U::AbstractVector{<:AbstractArray{T, Φ}}, D, τ, p, B, k; kwargs...)\n",
       "```\n",
       "\n",
       "Perform a spatio-temporal timeseries prediction for `p` iterations, using local weighted modeling [1]. The function always returns an object of the same type as `U`, with each entry being a predicted state. The returned data always contains the final state of `U` as starting point (total returned length is `p+1`).\n",
       "\n",
       "`(D, τ, B, k)` are used to make a [`STReconstruction`](@ref) on `U`. In most cases `k=1` and `τ=1,2,3` give best results.\n",
       "\n",
       "## Keyword Arguments\n",
       "\n",
       "  * `boundary, weighting` : Passed directly to [`STReconstruction`](@ref).\n",
       "  * `method = AverageLocalModel(2)` : Subtype of [`AbstractLocalModel`](@ref).\n",
       "  * `ntype = FixedMassNeighborhood(3)` : Subtype of [`AbstractNeighborhood`](@ref).\n",
       "  * `printprogress = true` : To print progress done.\n",
       "\n",
       "## Description\n",
       "\n",
       "This method works identically to [`localmodel_tsp`](@ref), by expanding the concept from vector-states to general array-states.\n",
       "\n",
       "## Performance Notes\n",
       "\n",
       "Be careful when choosing `B` as memory usage and computation time depend strongly on it.\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : U. Parlitz & C. Merkwirth, [Phys. Rev. Lett. **84**, pp 1890 (2000)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.84.1890)\n"
      ],
      "text/plain": [
       "```\n",
       "localmodel_stts(U::AbstractVector{<:AbstractArray{T, Φ}}, D, τ, p, B, k; kwargs...)\n",
       "```\n",
       "\n",
       "Perform a spatio-temporal timeseries prediction for `p` iterations, using local weighted modeling [1]. The function always returns an object of the same type as `U`, with each entry being a predicted state. The returned data always contains the final state of `U` as starting point (total returned length is `p+1`).\n",
       "\n",
       "`(D, τ, B, k)` are used to make a [`STReconstruction`](@ref) on `U`. In most cases `k=1` and `τ=1,2,3` give best results.\n",
       "\n",
       "## Keyword Arguments\n",
       "\n",
       "  * `boundary, weighting` : Passed directly to [`STReconstruction`](@ref).\n",
       "  * `method = AverageLocalModel(2)` : Subtype of [`AbstractLocalModel`](@ref).\n",
       "  * `ntype = FixedMassNeighborhood(3)` : Subtype of [`AbstractNeighborhood`](@ref).\n",
       "  * `printprogress = true` : To print progress done.\n",
       "\n",
       "## Description\n",
       "\n",
       "This method works identically to [`localmodel_tsp`](@ref), by expanding the concept from vector-states to general array-states.\n",
       "\n",
       "## Performance Notes\n",
       "\n",
       "Be careful when choosing `B` as memory usage and computation time depend strongly on it.\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : U. Parlitz & C. Merkwirth, [Phys. Rev. Lett. **84**, pp 1890 (2000)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.84.1890)\n"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?localmodel_stts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1mS\u001b[22m\u001b[1mT\u001b[22m\u001b[1mR\u001b[22m\u001b[1me\u001b[22m\u001b[1mc\u001b[22m\u001b[1mo\u001b[22m\u001b[1mn\u001b[22m\u001b[1ms\u001b[22m\u001b[1mt\u001b[22m\u001b[1mr\u001b[22m\u001b[1mu\u001b[22m\u001b[1mc\u001b[22m\u001b[1mt\u001b[22m\u001b[1mi\u001b[22m\u001b[1mo\u001b[22m\u001b[1mn\u001b[22m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "STReconstruction(U::AbstractVector{<:AbstractArray}, D, τ, B, k, boundary, weighting)\n",
       " <: AbstractDataset\n",
       "```\n",
       "\n",
       "Perform spatio-temporal(ST) delay-coordinates reconstruction from a ST-timeseries `s`.\n",
       "\n",
       "## Description\n",
       "\n",
       "An extension of [`Reconstruction`](@ref) to support inclusion of spatial neighbors into reconstructed vectors. `B` is the number of spatial neighbors along each direction to be included. The parameter `k` indicates the spatial sampling density as described in [1].\n",
       "\n",
       "To better understand `B`, consider a system of 2 spatial dimensions, where the state is a `Matrix`, and choose a point of the matrix to reconstruct. Giving `B = 1` will choose the current point and 8 points around it, *not* 4! For `Φ` dimensions, the number of spatial points is then given by `(2B + 1)^Φ`. Temporal embedding via `D` and `τ` is treated analogous to [`Reconstruction`](@ref). Therefore the total embedding dimension is `D*(2B + 1)^Φ`.\n",
       "\n",
       "## Other Parameters\n",
       "\n",
       "  * `boundary = 20` : Constant boundary value used for reconstruction of states close to the border. Pass `false` for periodic boundary conditions.\n",
       "  * `weighting = (a,b)` or `nothing` : If given numbers `(a, b)`, adds `Φ` additional entries to reconstructed states. These are a spatial weighting that may be useful for considering spatially inhomogenous dynamics. Each entry is a normalized spatial coordinate $-1\\leq\\tilde{x}\\leq 1$:\n",
       "\n",
       "    $$\n",
       "    \\begin{aligned}\n",
       "    \\omega(\\tilde{x}) = a \\tilde{x} ^ b.\n",
       "    \\end{aligned}\n",
       "    $$\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : U. Parlitz & C. Merkwirth, [Phys. Rev. Lett. **84**, pp 1890 (2000)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.84.1890)\n"
      ],
      "text/plain": [
       "```\n",
       "STReconstruction(U::AbstractVector{<:AbstractArray}, D, τ, B, k, boundary, weighting)\n",
       " <: AbstractDataset\n",
       "```\n",
       "\n",
       "Perform spatio-temporal(ST) delay-coordinates reconstruction from a ST-timeseries `s`.\n",
       "\n",
       "## Description\n",
       "\n",
       "An extension of [`Reconstruction`](@ref) to support inclusion of spatial neighbors into reconstructed vectors. `B` is the number of spatial neighbors along each direction to be included. The parameter `k` indicates the spatial sampling density as described in [1].\n",
       "\n",
       "To better understand `B`, consider a system of 2 spatial dimensions, where the state is a `Matrix`, and choose a point of the matrix to reconstruct. Giving `B = 1` will choose the current point and 8 points around it, *not* 4! For `Φ` dimensions, the number of spatial points is then given by `(2B + 1)^Φ`. Temporal embedding via `D` and `τ` is treated analogous to [`Reconstruction`](@ref). Therefore the total embedding dimension is `D*(2B + 1)^Φ`.\n",
       "\n",
       "## Other Parameters\n",
       "\n",
       "  * `boundary = 20` : Constant boundary value used for reconstruction of states close to the border. Pass `false` for periodic boundary conditions.\n",
       "  * `weighting = (a,b)` or `nothing` : If given numbers `(a, b)`, adds `Φ` additional entries to reconstructed states. These are a spatial weighting that may be useful for considering spatially inhomogenous dynamics. Each entry is a normalized spatial coordinate $-1\\leq\\tilde{x}\\leq 1$:\n",
       "\n",
       "    $$\n",
       "    \\begin{aligned}\n",
       "    \\omega(\\tilde{x}) = a \\tilde{x} ^ b.\n",
       "    \\end{aligned}\n",
       "    $$\n",
       "\n",
       "## References\n",
       "\n",
       "[1] : U. Parlitz & C. Merkwirth, [Phys. Rev. Lett. **84**, pp 1890 (2000)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.84.1890)\n"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?STReconstruction"
   ]
  }
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